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This article was published as a part of the Data Science Blogathon.


The r ers’ preferences and generates recommendations or proposals based on those preferences. Well-known websites like Facebook, LinkedIn, Instagram, Snapchat, Twitter, Amazon, Flipkart, and Netflix use different machine learning algorithms to draw people and increase their time spent on their web es and services.

Social Network Service Recommendation System

Streaming Service Recommendation System

Tourism Service Recommendation System

E-Commerce Service Recommendation System

Healthcare Service Recommendation System

Education Service Recommendation System

Every recomme rent approach. We’ll mostly be talking about Social Network Service recommendations in this article.


What is Recommendation System?

The Social Network Service Recommendation System uses several algorithms to suggest many people possible who may end up becoming friends with the user. The Link Prediction Method is undoubtedly a common type of algorithm for recommendations. Let’s talk about how recommendations are made by applying the Link Prediction Method.

Link Prediction Method

In Link Prediction Method, every single user is a node. Edges represent the relationship between two nodes. We can say that two nodes are related if there exists a relationship between them. As nodes and links can be represented in graphs, graph theory concepts can be used to make link predictions. Both directed and undirected graphs are possible. Because nodes in social networks can have followers and followings, the graph looks directed.

The link Prediction method should identify all probable connections from the source node to other nodes.


Every time a user signs in, our recommendation system must suggest all the source node’s neighbors

. We will recommend the nodes have the source node’s shortest paths of less than three.

The model should be trained using the given set of nodes together with their corresponding independent features. The model should predict all potential links to establish a connection if a new source node is made available. If a link can be established, recommendations from friends can be sent from the source node to the destination node.

We used the dataset from the Facebook Recruiting competition on Kaggle to train our model.


There is a training dataset and a testing dataset for the Facebook Recruiting competition on Kaggle. The training set has 9437519 rows and two columns (source node, destination node). The test set has 262588 rows and 1 column (source node). Here is a link to the dataset.


To find all functional connecting nodes (destination nodes) for the given source nodes in the test dataset.


When we look into the dataset, we only find two columns (source node, and destination node). There is no target variable. We are unable to develop a machine-learning model using these features. More features must be added. Noisy data may occur when the number of features rises. Our difficulty is to,

Make a target variable

Including features (Feature Engineering)

Eliminating irrelevant features (Feature Selection)

Let’s build our recommendation engine piece by piece.

Steps to Perform the Recommendation System

Importing necessary libraries

Exploratory Data Analysis

Creating Missed Connections

Getting train and test data after concatenation

Feature Engineering

Feature Selection

Model Training

Hyperparameter Tuning

Performance Measure

Importing Libraries

Matplotlib, and SKLearn.

Exploratory Data Analysis

Based on the dataset, we can draw certain conclusions.

 1. A Directional Graph

The source node 1 is linked to the destination nodes 690569, 315892, and 189226, according to the above table. We may say that the above graph is a directed graph because the link between the source and destination nodes is visible.

2. Number of unique nodes and links

The number of unique nodes and links is calculated.

g=nx.read_edgelist('train_unique.csv',delimiter=',',create_using=nx.DiGraph(),nodetype=int) print(

3. Number of followers(in_degree) & Following/Followee (out_degree)

    Follower (in_degree) :

 For the Destination node, the Source node is the follower.


For the source node, the Destination node is the followee/following

“indegree_no” gives the number of followers of each node.

indegree_no = dict(g.in_degree()).values()

Indegree values

number of followee/following of each node. 

outdegree_no = dict(g.out_degree()).values()

Outdegree values

“in_out_degree” gives the total number of followers + following of each node.

from collections import Counter dict_in = dict(g.in_degree()) dict_out = dict(g.out_degree()) total = Counter(dict_in) + Counter(dict_out) in_out_degree = np.array(list(total.values()))

4. The graph’s findings are:

14% of participants follow no one.

10% of people have no followers at all.

10% of people are more popular and have more followers.

A very small number, 1% of users, have more than 40 followers.

One percent of users—a relatively small percentage—have more than 72 followers.

No. of people with a minimum following and followers: 334291

No. of people with a maximum following and followers: 1

No. of people with less than 10 following and followers: 1320326

Components that lack a path between them: 32195

Creating Missed Connections

To formulate the target variable, we should create new edges called missed edged with the aid of exploratory data analysis. The following is our understanding:

We need data on linked and disconnected nodes (0 and 1) for the categorization problem.

Until now, we have only been given the details of neighboring nodes.

We should add details about the disconnected nodes.

The maximum number of links established with ‘n’ nodes: nC2

There are possibilities for 1862220 nodes to generate1862220C2 links, in which 9437519 are neighbors and are denoted by 1.

The remaining nodes can be represented by 0 and are unconnected.

possible not-connected nodes is 1862220C2-9437519.

The nodes with the shortest paths longer than two are our current focus.

The value of the disconnected nodes must always be zero because there is no connection.

Add a 0 to the new connection.

The following actions are implemented using the knowledge from above.

How to fill in missing edges:

    Read unique valued train dataset.

    as a key.

    ‘1’ represents that the source and destination are neighbors

    ‘-1’ represents that the source and destination aren’t related

    Look for nodes in the training set and mark them as ‘1’.

    Create an empty set to add not-connected links.

    Create source and destination nodes randomly, with a size of 1862220 each, and initialize them with -1 to signify that they have no relationship.

  1. The reason to choose 1862220 source and destination nodes is, that they have the potential to produce 9437519 links.

  2. Consider the following to complete the set of missing edges:
    • Edges shouldn’t have been joined before.

    • The source node and the destination node shouldn’t be the same.

      ###generating disconnected edges from given graph import random if not os.path.isfile('missing_edges_final.p'): #getting all set of edges r = csv.reader(open('train_unique.csv','r')) # r = pd.read_csv('train_unique.csv') edges = dict() for edge in r: edges[(edge[0], edge[1])] = 1 # marking edges present as 1 missing_edges = set([]) while (len(missing_edges)<9437519): a=random.randint(1, 1862220) b=random.randint(1, 1862220) temp = edges.get((a,b),-1) # marking random edges as -1 if temp == -1 and a!=b: try: missing_edges.add((a,b)) else: continue except: missing_edges.add((a,b)) else: continue pickle.dump(missing_edges,open('missing_edges_final.p','wb')) else: missing_edges = pickle.load(open('missing_edges_final.p','rb'))

      has an equal number of links.

      df_pos = pd.read_csv('train.csv') df_neg = pd.DataFrame(list(missing_edges), columns=['source_node', 'destination_node']) print("Number of nodes in the graph with edges", df_pos.shape[0]) print("Number of nodes in the graph without edges", df_neg.shape[0])

      and negative datasets into one dataset after equating the two datasets.

      The Shortest path

      The intuition of the shortest path is:

      Finding the shortest route between a source and a destination is made easier by using the shortest path.

      If A is the source and D is the destination in the image above, the shortest path between

      A – B = 1

      A – C = 2

      A – D = 3

      Compared to linking nodes A and D, connecting nodes A and C has a higher likelihood. The desired value should be 0; thus, to generate disconnected nodes, we employ pairs that could not be joined in the future. The nodes whose shortest paths above two are only considered.

      Getting Trained and Test Data after Concatenating Linked and Missed Nodes

      The formation of target variables is complete. The dataset should now be divided into training and testing sets. By dividing the dataset, we can do a performance analysis.

      Split train and test datasets from the concatenated dataset

      the test dataset.

      trY_teY = len(train_nodes_pos.intersection(test_nodes_pos)) trY_teN = len(train_nodes_pos - test_nodes_pos) teY_trN = len(test_nodes_pos - train_nodes_pos) print('no of people common in train and test -- ',trY_teY) print('no of people present in train but not present in test -- ',trY_teN) print('no of people present in test but not present in train -- ',teY_trN) print(' % of people not there in Train but exist in Test in total Test data are {} %'.format(teY_trN/len(test_nodes_pos)*100))

      Important learning made from the result of the above code is,

      Use a sample of the data from both datasets to train the model. We randomly picked 50,000 data from the test dataset and 100,000 data from the training dataset.

          filename = "train_after_eda.csv" n_train = sum(1 for line in open(filename)) n_train = 15100028 s = 100000 #desired sample size skip_train = sorted(random.sample(range(1,n_train+1),n_train-s))

      Feature Engineering

      We have only the source node and the destination node right now. To train a model, these two features are insufficient. We would like to add more features. Utilizing the relationship between the nodes, we will create more features.

      Jaccard Distance:

      The likelihood of a link between the source node and the destination node is measured by the Jaccard distance. We can calculate this distance for both followers and following.

      Let A be a set of followers of the source node

      Let B be a set of followers of the destination node

      A = {1,5,7,9,11,13}, B = {3,11,13,15,17,19}

      Jaccard Distance = 2/10

      The probability of connection between A and B is 0.2

      The likelihood of a connection between source and destination nodes is 0.2 and grows with the number of common nodes. A connection between nodes is more probable if the Jaccard distance is high.

      #for followee/following def jaccard_for_following(a,b): try: return 0 sim = (len(set(train_graph.successors(a)).intersection(set(train_graph.successors(b)))))/ (len(set(train_graph.successors(a)).union(set(train_graph.successors(b))))) except: return 0 return sim

      Cosine Similarity:

      The similarity between two links is quantified by cosine similarity. It’s represented by the cosine angle between them. The likelihood of a connection is higher if the cosine angle between the source and destination is zero or close to zero.

      For the above example, cosine similarity for followees/following can be calculated as:

      K = 2 / √36 ; K = 0.667

      #for followees def cosine_for_following(a,b): try: return 0 sim = (len(set(train_graph.successors(a)).intersection(set(train_graph.successors(b)))))/ (math.sqrt(len(set(train_graph.successors(a)))*len((set(train_graph.successors(b)))))) return sim except: return 0

      Page Rank:

      Page Rank is determined by two factors: the number of links and the quality of links.

      The number of links

      It counts the number of incoming links (followers)

      Quality of links

      It considers the incoming links (followers) that already have more incoming links (followers).

      pr = nx.pagerank(train_graph, alpha=0.85)

      Shortest Path:

      Based on the weights, the shortest path will determine the shortest route from the source node to the destination. If there exists a direct link between the source and the destination node, we will delete that link. This direct link won’t contribute much to prediction.

      #if has direct edge then deleting that edge and calculating shortest path def compute_shortest_path_length(a,b): p=-1 try: if train_graph.has_edge(a,b): train_graph.remove_edge(a,b) p= nx.shortest_path_length(train_graph,source=a,target=b) train_graph.add_edge(a,b) else: p= nx.shortest_path_length(train_graph,source=a,target=b) return p except: return -1

      Weakly Connected Component:

      Here neither the source nor the destination can be reached from the other.

      wcc gives a list of weakly connected components.

      We need to check if the source and destination nodes are part of the wcc


      in wcc, remove that link and find the shortest path.

      If both the nodes are in wcc, assign 1.

      If one of the nodes is not in wcc, assign 0.

      #getting weekly connected edges from graph wcc=list(nx.weakly_connected_components(train_graph)) def belongs_to_same_wcc(a,b): index = [] if train_graph.has_edge(b,a): return 1 if train_graph.has_edge(a,b): for i in wcc: if a in i: index= i break if (b in index): train_graph.remove_edge(a,b) if compute_shortest_path_length(a,b)==-1: train_graph.add_edge(a,b) return 0 else: train_graph.add_edge(a,b) return 1 else: return 0 else: for i in wcc: if a in i: index= i break if(b in index): return 1 else: return 0

      Adar Index:

      The Adar Index calculates the number of common links that two nodes share.

      It uses the formula:


      N(y) = Followers and following of y


      N(y) = {e1, e3,e7,e9,e11}

      N(x)∩N(y) = { e1,e3,e7}

      For every edge of N(x)∩N(y), we should take the inverse log and sum them.

      N(x)∩N(y) will give two types of insights.

      Celebrity Link

      Since both follow celebrities, there will be some common links between the source and destination nodes. It does not necessarily follow that both nodes are friends. The inverse log decreases the Adar index value and hence lowers the likelihood of a friend recommendation.

      Normal Link:

      There may be a potential for both nodes to be friends given the small number of common links between the source and destination nodes. The Adar index’s value rises after applying inverse log, thereby increasing the likelihood of being a friend and will recommend you.

      #adar index def calc_adar_in(a,b): sum=0 try: n=list(set(train_graph.successors(a)).intersection(set(train_graph.successors(b)))) if len(n)!=0: for i in n: sum=sum+(1/np.log10(len(list(train_graph.predecessors(i))))) return sum else: return 0 except: return 0

      Follow Back:

      It’s been believed that if there is a connection between the source and the destination, there exists a connection between the destination and the source. If such a connection exists, assign 1, and if not, 0.

      train_graph.has_edge (b, a), then return 1; otherwise, return 0.

      def follows_back(a,b): if train_graph.has_edge(b,a): return 1 else: return 0

      Katz Centrality:

      It calculates the relative influence by counting the number of neighbors and those who are linked to the node. It is employed to assess a node’s relative power among the nodes.


          katz = nx.katz.katz_centrality(train_graph,alpha=0.005,beta=1)

      HITS(Hyperlink-Induced Topic Search) Score:

      They go by the names hubs or authority.

      Using the HITS score we can extract authority and hub value.

      It is based on a webpage rating.

      Authorities: The node values that are supported by many other hubs

      Hubs: The node value supported on outgoing links

          hits = nx.hits(train_graph, max_iter=100, tol=1e-08, nstart=None, normalized=True)

      Weight Features:

      Edge weight is necessary to assess the similarity between nodes. The weight lowers when the number of links increases, and vice versa.

      for i in tqdm(train_graph.nodes()): s1=set(train_graph.predecessors(i)) w_in = 1.0/(np.sqrt(1+len(s1))) Weight_in[i]=w_in s2=set(train_graph.successors(i)) w_out = 1.0/(np.sqrt(1+len(s2))) Weight_out[i]=w_out

      Preferential Attachment:

      In social networks, people who have more friends are more inclined to continue adding friends to their side. By dividing the number of friends a node has, we may determine how rich it is. Both the following and the followers will benefit from this.

      def Preferential_Att1_Followers(x,y): try: return 0 score=(len(set(train_graph.successors(x)))*len(set(train_graph.successors(y)))) return score except: return 0 Feature Selection

      We have added many new features through feature engineering. Some may be unimportant or noisy, making the machine learning model perform worse. During the deployment, adding new features will make the algorithm more difficult. Maintaining features that provide the best performance is crucial. Techniques for feature selection assist us in doing so. Here, the feature selection is made using the Recursive Feature Elimination with Cross Validation.

      Recursive Feature Elimination with Cross Validation:

      Working Steps:

      Constructs a model with all the features.

      Deletes features with less importance.

      Rebuilds the model with the remaining features.

      Repeat this until no more features can be discarded.

      Why RFECV:

      Reduces model complexity

      Easy to configure and use

      Collinear and linearly dependent features are removed automatically.

      Returns the optimal number of features for the model

      Captures all features important for model training

      Here, the REFCV algorithm returned 20 important features.

      from sklearn.feature_selection import RFECV rfecv = RFECV(estimator= XGBClassifier(random_state=25,n_jobs=-1), cv = 5) df_final_train,y_train) X1_cv = rfecv.transform( df_final_train ) X2_cv = rfecv.transform( df_final_test) f = rfecv.get_support(1) f Model Training

      Our model needs to be trained now. Our model was trained using 4 different machine learning algorithms.





      Hyperparameter Tuning the Recommendation System

      To identify the best parameters, we performed hyperparameter tuning. The results are tracked.


      The following parameters were used to evaluate the performance of the XGBoostClassifier Algorithm.

      param_dist = {"n_estimators":sp_randint(105,125), "max_depth": sp_randint(1,10), "alpha": [0.001,0.01,0.1], "learning_rate":[0.001,0.01,0.1]} clf = XGBClassifier(random_state=25,n_jobs=-1) rf_random = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=5,cv=10,scoring='f1',random_state=25,return_train_score=True),y_train)

      Random Forest classifier:

      Using the following parameters, the RandomForestClassifier Algorithm’s performance was assessed.

      param_dist = {"n_estimators":sp_randint(105,125), "max_depth": sp_randint(10,15), "min_samples_split": sp_randint(110,190), "min_samples_leaf": sp_randint(25,65)} rf_clf = RandomForestClassifier(random_state=25,n f_random = RandomizedSearchCV(rf_clf, param_distributions=param_dist, n_iter=5,cv=10,scoring='f1',random_state=25,return_train_score=True),y_train)

      KNearest Neighbor:

      Using the following parameters, the KNearestNeighbor Algorithm was analyzed for improved performance.

      param_dist = {"n_neighbors":sp_randint(5,10)} k_clf = KNeighborsClassifier(n_jobs=-1) rf_random = RandomizedSearchCV(k_clf, param_distributions=param_dist, n_iter=5,cv=10,scoring='f1',random_state=25,return_train_score=True),y_train)

      Support Vector Machines:

      param_dist = {"alpha": sp_randint(1,3)} svm_clf = SGDClassifier(n_jobs=-1) rf_random = RandomizedSearchCV(svm_clf, param_distributions=param_dist, n_iter=5,cv=10,scoring='f1',random_state=25,return_train_score=True) df_final_train=df_final_train.astype(float),y_train) Performance Measure of Recommendation System

      The most important part of the recommendation system is the overall performance metric. We’ve used a variety of models to get performance scores. Choose the one that works for you.

      We have computed the f1 score before and following the feature selection, and the values are given below.


      Performance (F1 Score)

      All Features

      Optimized Features






























      Through article, we’ve gained knowledge about

      Performing Exploratory Data Analysis

      Techniques to seek out missing edges (to create new friend suggestions)

      Feature Engineering

      Feature Selection

      Hyperparameter tuning

      Performance Analysis

      This recommendation system heavily relies on feature engineering and feature selection. Without feature engineering, the dataset cannot be used to train the machine learning model. To add characteristics to the dataset, we have employed various related techniques. The dataset will blatantly contain unnecessary or noisy information when adding more features. The performance increases when these features are removed. Furthermore, adding too many features will complicate deployment at some time. Therefore, it’s crucial to maintain features that deliver the best performance. To return the most helpful features, we employed the recursive feature elimination cross-validation algorithm. The model’s overall performance with both its default features and its optimized features is measured. We have shown that the feature selection increased the performance. Therefore, we can conclude that feature engineering and feature selection are crucial to improving a system’s overall performance.

      The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 


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      Real estate social media strategy template

      Building a real estate social media strategy from the ground up is never easy, so we created this extensive template to help you get started.

      Simply make a copy of this document and tailor it to your needs. Then, you’ll be ready to take the realtor social media world by storm.

      Inspiring examples of real estate social media marketing

      Still wondering how to market real estate on social media? Here are some of the best real estate agent social media campaigns we’ve seen.

      1. Graham Stephan

      With over 4.1 million subscribers, real estate YouTuber Graham Stephan is one of the best-known names in real estate social media. But, you’ll be surprised to see much more than simple listings and walkthroughs on his channel.

      Graham knows that his viewers are interested in growing their wealth, so he gives tips, tricks, and insights into his own life, and how he became a multi-millionaire at twenty-six. Graham’s content is a good reminder that it’s not only clients that come to your channels. Consider how you might use content to inspire the next generation of realtors. And, grow your personal brand at the same time.

      2. Oakwyn Realty

      The best brands know that you don’t need to slap your logo on something in order for people to recognize it as yours. Oakwyn Realty, a real estate firm in Vancouver, BC, knows this better than most.

      Instead of posting home walkthrough videos, or agent achievements, Oakwyn focuses on promoting its values, inspiring its agents, and cultivating a strong vibe with its social media content.

      Take this post, for example, which offers a simple, natural visual, paired with an inspiring quote. Does this have anything to do with real estate? Maybe not. But, it does paint Oakwyn as a brand that cares about its agent’s health and success–and that’s something buyers, sellers, and agents will want to get behind.

      Oakwyn is a great example of how focusing on content, rather than sales, can level-up your realtor social media marketing.

      3. Engel & Völkers

      Buying a home is about more than just a stunning property. Homes mean memories, big life events, security, and more.

      When real estate brokerage Engel & Völkers makes property walkthrough videos, they go beyond simply showing off the bedrooms and bathrooms. They show you what your life would look like if you lived here, from weddings, to raising children, and everything in between.

      Use your real estate social media content to place your clients in the home, and see how fast those sales come through.

      4. West Haven Group

      We’ve already talked about the importance of educating real estate clients through social media. One of the best ways to do this is through regular market updates, like this one from West Haven Group.

      Market updates help people buying and selling real estate gauge whether now is the time to invest or sit still. They also position your firm as experts in the real estate field.

      You never know when a potential client will need a nudge in the right direction, so be sure to share your knowledge and industry insights widely.

      5. Miranda Caldwell

      Video platforms like TikTok and YouTube Shorts thrive on short, snackable content. So, why not use them to show off the best, most interesting features of your house?

      Miranda Caldwell does real estate TikTok right with these short, intriguing videos that keep her viewers watching. Consider how you might leverage unique or different features of your property on social media

      Real estate social media FAQs What social media is best for real estate sales?

      The best social media channels for real estate agents depend on who your target audience is. According to the National Association of Real Estate Agents the majority of home buyers are older millennials and Gen Xers. These audiences are more likely to use platforms like Facebook, Instagram, Twitter, and LinkedIn.

      Source: National Association of Real Estate Agents

      How is social media used in real estate? What should a new realtor post on social media?

      New realtors should focus on creating content that showcases their expertise in the field, provides helpful tips and insights to potential buyers, and promotes their listings. As well, it’s important to share content from other realtors or industry professionals in order to build relationships within the community.

      Get Started

      Get new clients and close more deals with Hootsuite, the #1 social media management tool for real estate agents.

      The 2023 Guide To Social Media Reputation Management

      Social media reputation management isn’t about controlling the narrative — it’s about listening, learning from feedback, and responding in a way that builds trust in your brand.

      Jeff Bezos is credited with saying “Your brand is what people say about you when you’re not in the room.” In the age of social media visibility and virality, what people say about you can speed its way to millions of people. Or at least be visible to your next potential customer. And that’s why social media reputation management is so important.

      Reputation management isn’t about controlling the narrative. It’s about listening to what customers say, learning from their feedback, and responding in a way that builds trust in your brand.

      The stakes are high. Over 2,000 executives surveyed attribute 63% of their companies’ value to their online reputations. Most consumers (93% according to this survey) say online reviews affect their buying decisions. Even hiring is affected as 86% of employees check reviews and ratings before applying for a job.

      This is not a tale of woe. It’s a hero story starring you. By reading this post, you’ll have at your disposal the tools and strategies to “hear” what your customers say about your brand. You’ll be able to respond in real time. And you’ll create an online reputation that endears your company to your customers.

      Bonus: Download a free guide to learn how to use social media listening to boost sales and conversions today. No tricks or boring tips—just simple, easy-to-follow instructions that really work.

      What is social media reputation management?

      Social media reputation management is the practice of observing and shaping how people perceive your brand on social media platforms like Facebook, Twitter, and TikTok.

      Without attention and guidance, your reputation shapes itself. Maybe that’s OK if everyone interacting with your company has a great experience.

      Unfortunately, people are more likely to share a negative customer experience than a positive one. And sometimes, someone will spread misleading information. So you could end up with a poor reputation on social media, even if the vast majority of your customers love you.

      With a reputation management plan, you’ll know when someone calls out your brand and the mood of that mention. Plus, you’ll have a plan to address it. You can shout positive messages far and wide. And address the negative mentions before they become full-blown public relations crises.

      A social media reputation management strategy typically consists of three components:

      An audit that surfaces your current reputation online

      Ongoing qualitative and quantitative monitoring of social channels

      Proactive and reactive messaging to build, improve, or repair customer trust

      With that big-picture context in mind, let’s look at the steps you’ll take to build a loyal and loving fanbase on social media.

      How to manage your reputation on social media

      A good social media reputation management plan starts with listening to what people have to say about your brand. It continues with steps to mitigate negative sentiment. It includes ways to humanize your brand and promote the positive vibes your fans share.

      Monitor brand mentions and conversations

      Social media brand monitoring and sentiment analysis are at the heart of all online reputation management efforts.

      Brand monitoring tracks all the social media posts and conversations that include a mention of your brand, products, and key spokespeople (like your CEO). Some people @ your company directly, but many don’t. So you’ll need to track branded #s and untagged mentions as well.

      One thing about me, if I’m already late I’m going to Starbucks

      — Bedazzled Stud ✨ (@csweetnsour) April 18, 2023

      Remember to track common misspellings. If the team at Starbucks didn’t do that, they’d miss out on some interesting conversations.

      Need social media monitoring tools to find these brand conversations? Hootsuite has you covered.

      Sentiment analysis is the contextual review of brand mentions. It goes beyond counting how often your company is discussed on social media. It also considers the mood and opinion behind those conversations.

      Say thousands of people talk about your product’s performance.

      Sentiment analysis takes cues from their language to give you an idea if the mood is positive or negative.

      A quantitative analysis of what you gather will tell you your social share of voice, social ROI, and your social sentiment score. From qualitative analysis, you’ll learn what hashtags and trends people associate with your brand.

      Monitoring conversations about your brand online doesn’t just surface superficial sentiment. You’ll uncover all sorts of ways to improve your business — even learning how to make your product better. Which is exactly what The Isle of Paradise did here.

      Listen for competitor and keyword mentions

      What happens to your competitors and throughout your industry can also affect your online reputation. Remember the viral video of security officers dragging a United Airlines customer off of an overbooked plane? Other carriers felt the heat and had to change their overbooking policies.

      That’s where social listening comes in. It goes beyond looking for mentions of your brand. It also tracks conversations that include your competitors and market-related keywords.

      Have a look at how gaming chair maker Mavix jumped into this thread.

      If anybody’s looking for a gaming chair alternative after watching this, look no further😈

      — Mavix Chairs (@mavixchairs) April 18, 2023

      Someone tweeted an unflattering video of their competitor. Mavix’s soft-touch trolling gathered interest and good feelings from gamers who needed a new seat. It’s a situation made possible first by active social media listening.

      You should also set your software to listen for important keywords like H&R Block did here.

      They found an opportunity to join an ongoing conversation. Even though it didn’t mention their company. And got to Tweet about an adorable pup doing taxes.

      Engage and respond quickly

      It’s not enough to listen to social media chatter about your brand. You must also respond, and do it fast. Why? 75% of U.S. consumers expect brands to reply to social media questions and complaints within a day. 20% think the response should be immediate!

      Every mention is an opportunity to increase engagement on social media and build trust for your brand. Some conversations will be fun and friendly. Others may start because your customer is frustrated or confused. Social media listening tools will help you find and reply to these conversations fast. Now, let’s look at how you can respond to the full range of social media interactions.

      For starters, you can turn your social media account into a public-facing customer service center. That’ll help ease customers’ confusion. Many people turn to social media when they need help, so it’s a natural place to answer questions and give guidance.

      Nike, for example, has a dedicated Twitter account it uses to solve its customers’ dilemmas.

      Any update on this? I am still having this issue…

      — J. Strand (@DJ_Ajaxx) September 15, 2023

      Nike doesn’t only use @nikeservice to answer individual questions, though. The brand also posts general updates that act like an ongoing FAQ section. Followers can find solutions before they even have to ask a question. Which means less burden on their customer service reps.

      Sometimes, people talk about your brand in amusing and positive ways. Those are great opportunities to drop in and join the conversation. Wendy’s rarely misses a chance to talk square burgers or…historical architecture?

      Why the sunrooms, Wendy. Spit some history my way!

      — jaxsaid 🐌 (@jaxsaid) April 18, 2023

      Not all social media posts are going to be positive, though. When a customer does share their disappointment, it’s important to help them quickly.

      I’m not sure why @dominos has had me on hold for this long or why my food is still in “prep” an hour later but okay

      — liv (@oliviafyeager) April 25, 2023

      No matter which type of conversation you encounter, the key is to remember that there’s a person on the other end. Think about what you’d say to them if they were standing in front of you. Use that to help make your responses more human.

      Reduce response time (and your workload)

      Manage all your messages stress-free with easy routing, saved replies, and friendly chatbots. Try Hootsuite’s Inbox today.

      Book a Demo

      Enable and respond to reviews

      When nurtured and managed, online reviews are a great type of grassroots, word-of-mouth marketing. They’re also an important part of cultivating your social media presence. A Brightlocal survey found that nearly half of consumers look at Facebook reviews.

      The same survey also revealed that buyers think it’s important for brands to have at least a four-star rating. And more than half of buyers say they want to see both positive and negative reviews from the last two to four weeks.

      You’ll need a steady stream of positive reviews. To get them, make sure you’ve turned on Facebook recommendations. Then don’t be afraid to ask. Over 50% of shoppers are “likely” or “highly likely” to leave a review if the business asks them to. And what’s the most effective way to request a customer review? It’s a pretty close race between email, in-person, on a receipt, and through social media, according to the survey.

      Gathering a bunch of recent 5-star reviews is great. But if you really want to wow customers, reply to your reviews. 88% of consumers said they’re “likely to use a business if they can see the business owner responds to all reviews, whether positive or negative.”

      When you do receive a bad review, you’ve got to tackle it head-on. It’s a signal to all customers that you’re willing to solve whatever issue may come up.

      A good rule of thumb is to address bad reviews publicly. But then solve them privately. Those conversations may contain private information. That’s especially important if you work in a regulated industry like healthcare.

      But don’t be shy about sharing the wins, either. You’ve worked hard to deliver a product or service people love. Use their hard-won feedback to earn more business by placing it on social posts or your website.

      Cultivate your reputation proactively

      You don’t need to wait for a customer to post about you. There are ways to establish and manage your social media reputation proactively.

      One option is to publish content on your channels that shows the world what your brand is all about. Take the Irish leader of low-cost air travel, for example. The company is infamous for cut-rate services, but one look at its TikTok account and it’s clear that Ryanair just doesn’t care.

      Ryanair doesn’t stop there. Its social team has masterfully spun its bad reputation into a Stockholm Syndrome-like relationship with the very people that malign them online.

      Source: Ryanair’s TikTok

      The tricky balance of snark and savagery is just the ticket for RyanAir. Its customers know full well they’re not getting a luxury ride, so any complaints about service can be met with a “see, we told you” attitude.

      GoPro looks at social media reputation through a different lens. They don’t tell shoppers what their brand is. They ask current users to show it.

      GoPro gathers UGC (user-generated content) by asking customers to tag images and video posts with a hashtag like #gopro. Consumers trust authentically created UGC over other types of content. So this strategy generates a lot of confidence in the GoPro brand.

      Set up (and enforce) brand guidelines

      No matter if your brand is snarky, serious, or somewhere in between, consistency is the key to maintaining your reputation online. A rogue employee posting off-brand replies can damage your reputation in no time.

      Source: Facebook

      In this case, it’s a fake “customer support” Facebook account trolling people. But you want to avoid a situation where your employees respond in a way that hurts your reputation. So you’ll need to create both a social media style guide and a social media policy.

      The social media policy outlines big-picture expectations for how your employees post and engage on branded social media channels (or when they’re posting on personal accounts on behalf of your brand). The policy covers content types, how to respond to positive and negative feedback, legal compliance, and more. Here’s a guide to help you write your social media policy (it includes a free template to help you get started).

      A social media style guide answers questions about how you’ll craft individual posts. What tone will you take on Twitter, LinkedIn, and Facebook? Are there buzzwords and jargon you want to use (or stay away from)? And, of course, are emojis part of the plan?

      Need more? This in-depth post will show you everything your social media style guide needs to have.

      Social media reputation management software

      Social media is a big place. It’s impossible to surface all the relevant conversations you should be a part of. These tools will help you find, analyze, and reply to mentions that matter to your business.


      Hootsuite makes it easy to track and respond to brand, competitor, and keyword mentions across all your social media accounts.

      Try for free

      The secret is Hootsuite Streams, a versatile way to view a variety of social media activities in one convenient space. You can create a stream to monitor keywords, brand or competitor mentions, and hashtags.

      Once your streams are up and running, you can jump into important conversations by replying from your dashboard. So when someone shares your latest Tweet or talks about your great service, you can reply quickly without opening multiple platforms.

      Private messages and DMs

      Public messages and posts on your profiles


      Emoji reactions

      … and more.

      The all-in-one agent workspace makes it easy to 

      Track the history of any individual’s interactions with your brand on social media (across your accounts and platforms), giving your team the context needed to personalize replies

      Add notes to customers’ profiles (Inbox integrates with Salesforce and Microsoft Dynamics)

      Handle messages as a team, with intuitive message queues, task assignments, statuses, and filters

      Track response times and CSAT metrics

      Free demo

      Plus, Inbox comes with handy automations:

      Automated message routing

      Auto-responses and saved replies

      Automatically triggered customer satisfaction surveys

      AI-powered chatbot features


      Brandwatch is a social listening platform that searches through billions of social media interactions. It delivers insightful data about your brand’s reputation.

      With Brandwatch, you learn what words are most commonly associated with your brand. You also know how often people talk about your company. And then you see the sentiment behind all those conversations.

      Brandwatch is fully integrated with Hootsuite. You can view and respond to big-picture trends and individual instances from one place.


      ReviewTrackers rounds out your social media reputation management toolbox by giving you a convenient space to see and engage with your online reviews.

      Source: ReviewTrackers

      If you use the ReviewTrackers plugin on Hootsuite, you can arrange your reviews into streams the same way you do social media mentions and posts. And also, like other streams, you can respond to your reviews in real time without leaving the platform. Then when those 5-star raves start rolling in, just hop over to your Hootsuite Creator workspace and share them across all your social channels.

      Save time managing your social media presence with Hootsuite. From a single dashboard, you can publish and schedule posts, find relevant conversions, engage the audience, measure results, and more. Try it free today.

      Get Started

      Manage all your social media in one place, measure ROI, and save time with Hootsuite.

      Beginners Guide To Itunes – Traditional Network Sharing And Home Sharing

      Sharing your library on the web carries several benefits. If you have more than one computer at home, you can share your iTunes music libraries from one computer to another. Besides, you can create separate libraries on each computer, or you can have all your music saved on a single computer that works as a music server for your other computers. Added to it the ability to share your music so others can enjoy your music on other computers. In iTunes, we can find two functions that allow sharing of songs at home or on the network – the traditional sharing function and the so-called “Home Sharing”.

      Share iTunes libraries on a local network

      Turning iTunes sharing on features is a simple procedure , just go to iTunes menu ( in the left hand corner) and open your preferences window. Then, a new window appears with options to configure sharing. In the “General” tab you can specify the name of the library – which will be visible to other computers. Then Select the sharing icon at the top of the list.

      At this point, your library should now be available for other users. Our library will be displayed for others on the list of iTunes libraries. Keep in mind, that if you want your library to be viewed by others, you have to have open iTunes.

      Home Sharing

      If you do not want to make your library available to all users on local network, then the iTunes offers a great way to stream or transfer music, movies, and more among up to five authorized computers in your household. It is a Home Sharing. To do so, you are required to use the same Apple ID. Users are allowed to stream music and video from one authorized computer to another, or simply drag music between computer libraries. We can also set iTunes to automatically sync purchases we make in iTunes store for any of our Home Sharing computers to our own library.

      The first step to sharing music within iTunes is to go to iTunes settings (in the left hand corner), access Library and select Turn On Home Sharing.

      Traditional sharing vs Home Sharing

      Both ways of sharing iTunes content give a very similar effect and require computers located within the same local network. When talking about traditional sharing, we do not have to log in on the same account on all computers, and therefore can stream music quickly and with no configuration for other computers on the network – without any restrictions.

      Home Sharing, on the other hand, works only within the computers on the same local network, and requires you to log on the same Apple ID account. It is more time consuming, however it provides some additional features such as automatic synchronization between local library and all purchased products from the iTunes Store. With the Home Collection, we are ensured that the library is available only for the computers that are authorized.

      A Comprehensive Guide On How To Make A Business Plan Presentation

      blog / Business Management A Comprehensive Guide on How to Make a Business Plan Presentation

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      An effective business plan presentation is key to attracting investors and securing funding for your business. So what are the fundamentals that go into creating a winning deck? This guide is a complete breakdown of the various elements of a business plan presentation, what slides you need to add, and a downloadable template you can follow to create your presentation. Before diving into that, let’s briefly recap what a business plan is and why you must create one. 

      What is a Business Plan?

      A business plan is a formal written document that outlines the company’s goals, objectives, and strategies. It serves as a blueprint for the business and can be aimed at either internal or external audiences. It is used to secure funding from investors or to guide the decision-making and direction of the company. 

      A business plan typically includes an executive summary, company description, market analysis, organizational structure, product, and service offerings, marketing and sales plans, and financial projections. Whether it is new or already established, every company should have a business plan that is periodically reviewed and updated. 

      Objectives of Creating a Business Plan

      The main purpose of a business plan is to serve as a roadmap for a business’s success and provide a clear picture of the company’s goals, strategies, and financial projections. Here are some of the objectives for a business plan: 

      Monitoring Progress

      A business plan can be a benchmark for measuring success and helps track progress toward goals. Furthermore, this plan is frequently reviewed to reflect on the goals and modify the plan accordingly. 

      Providing a Roadmap

      A roadmap you can follow to manage your business, prioritize tasks, allocate resources, focus on your priorities, plan a course of action, and make effective decisions. 

      Securing Funding

      An investor looks at many things before investing in a business. A plan can be a valuable tool to show the investors the purpose, potential, and future direction of your business and help you secure funding. 

      Communicating Strategy

      A good plan provides a way to communicate the company’s vision, mission, and strategies to all stakeholders, including employees, customers, and partners.

      Attracting Talent

      You need top talents to execute your plans and help you scale your business; a comprehensive and well-structured business plan can be used as a recruiting tool to help you create a great team. 

      Key Elements of Creating a Business Plan Presentation Company Overview

      Start with a basic introduction and overview of your company with contact information to provide the key information and help the audience understand what your business does. 


      This section details the major problems your target customers are experiencing. These are the issues that your business is expecting to solve with your product or service. 


      Now that you have established the problem, you must outline the solutions. Explain how your product or service will address these issues and emphasize the solution’s scalability. 

      Financial Plan

      A business plan presentation should describe the company’s expenses, profits, budget, and plans. For startups, this financial plan can predict the company’s future financial plans and how it will meet goals while staying within budget.

      Operation Plan

      The operation plan describes how your business will function regularly. This section will highlight the logistics and steps to achieve your business goals, including your operation’s resources, capital, and expense requirements. 

      Marketing Strategies Team

      This section introduces the project team members who will help build your business plan; it includes their qualifications, background, roles, and responsibilities. 


      A timeline is one of the key elements of any business plan presentation, as it outlines the future and key milestones your business hopes to accomplish. The timeline gives stakeholders, including investors, the assurance and conviction that you will successfully execute your business plan.

      In addition to these key elements, a business plan presentation can include visuals such as charts, images, and infographics to help illustrate the information efficiently. 

      Ten Slides You Need in Your Business Plan Presentation

      Here are ten slides that are commonly included in a business plan presentation:


      A brief explanation of the purpose of the presentation 

      Company Description:

      An overview of the company 


       The problem the business is solving 


      How will the business provide the solution to the problem 

      Product/Service Offerings:

      Description of the products or services

      Target Market:

      An overview of the potential customers, their demographic, needs, and preferences

      Marketing Strategies:

      The company’s plans for reaching, selling, and retaining its target market

      Financial Planning:

      Outline of the capital requirements, budget, and major expenses


       Introduction to the key team members 


       Projection for the future and an estimate of project completion 

      Also note that these slides are not set in stone, and the number and content may vary depending on the company, its goals, and its audience. The most important thing is to communicate the key elements of the business plan effectively and persuasively.

      Business Plan Presentation Example (Downloadable PPT)

      Here is a simple example of a business plan presentation that you can modify to fit the specific needs of your business. You can follow our downloadable presentation or the template below to create your presentation.

      #1 Slide: Introduction

      A brief overview of the company

      Contact information

      Purpose of the presentation

      Brief explanation of the product/service offered

      #2 Slide: Problem 

      State the problem you are trying to solve

      Explain why it is a problem 

      #3 Slide: Solution 

      Provide the solution 

      Explain why it is a great solution

      #4 Slide: Product/Service

      A detailed explanation of the product/service

      Unique selling proposition

      List of features and benefits

      #5 Slide: Market Analysis

      Identification of target market

      Competitor analysis

      Market size and growth potential

      #6 Slide: Marketing Strategy

      Explanation of the marketing plan

      Advertising and promotion plan

      Sales plan

      #7 Slide: Financial Planning

      Income statement 

      Balance sheet

      Cash flow statement

      Break-even analysis

      #8 Slide: Team

      Introduction of key team members

      Description of their experience and qualifications

      Explanation of roles and responsibilities

      #9 Slide: Timeline 

      Project schedule 

      Launch of marketing campaigns 

      Timeframe for each key milestone

      #10 Slide: Conclusion

      Ask for questions 

      Next steps, final thoughts, and future outlook

      Summary of key points


      ALSO READ: How Angel Investing is Funding the Future One Great Idea at a Time 

      Bottom Line

      The main goal of a business plan presentation is to effectively communicate your business’s value and potential and persuade the audience to support the company’s goals. A great business plan presentation is one of the most essential tools for entrepreneurs seeking to scale their business or land initial funding to get their business off the ground. Along with the above elements, be sure to create your presentation in simple and clear language with a clear structure and compelling visuals to engage investors. Explore Emeritus’ Business Management Courses to learn more about this subject.

      By Krati Joshi

      Write to us at [email protected]

      How To Record System Audio On Quicktime

      The screen recording option in Quicktime is a useful feature for you to record a part of your screen or your entire screen in high quality for whatever purpose. Quicktime also allows you to record your audio through the built-in mic, thus you can record anything ranging from tutorials to simple videos explaining an issue you’re facing on your system. However, in the case you want to record an online video or clip, you’ll find that there’s no option to record your Mac’s audio output by default. You’re stuck recording the voice from your Mac’s built-in mic.

      To solve this problem, Matt Ingalls (GitHub) has created an addon named Soundflower that adds an audio extension to macOS. By using this extension as your audio output source, and input source in Quicktime, you’ll be able to record audio directly from your system, bypassing the built-in mic. It’s way easier than it sounds. Let’s check it out.

      2. Once downloaded, open the DMG file, hold down the Control key on your keyboard, and open up the installer. Install the addon.

      Initially, you might face an error in the setup, as the Soundflower installer is not signed. You’ll need to allow it to be installed from the System Preferences app.

      Once you have Soundflower installed, simply follow the guide below to record system audio in your screen recording:

      2. From the Output pane, select Soundflower (2 ch) as your output. You’ll notice that if you play any audio on your system, you won’t hear it at all.

      3. Now, open Quicktime and select “New Screen Recording” from the File menu.

      If you already have an audio playing, you’ll notice the sound level show up in Quicktime.

      5. Start your recording and specify the part of the screen you want included in the recording.

      That’s it! Soundflower is indeed a handy tool to quickly include system audio output on your Quicktime recordings. Let us know if the method worked for you in the Comments section below.

      Shujaa Imran

      Shujaa Imran is MakeTechEasier’s resident Mac tutorial writer. He’s currently training to follow his other passion become a commercial pilot. You can check his content out on Youtube

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