Sentiment Analysis of Social Media

Tools and Features of Sentiment Analysis

Source: jjbuzzcloud.com

What is Social Media Marketing?

Social media marketing (SMM) is a form of digital marketing that involves sharing content on social media platforms in an attempt to actualize a firm’s branding, sales, and web traffic goals. Social media has become essential to helping brands connect to a wider range of customers, establish brand presence, and increase sales both in-store and online.

Brand Awareness

Brand awareness refers to extent at which customers are able to identify or recognize a brand’s product by its name. This is important because the higher the level of brand awareness the higher the sales of the brand’s products or services are likely to be. Brand awareness also increases market share, and can be a useful tactic when trying to promoting new products or trying to revive older brands.

What is Sentiment Analysis?

Also known as opinion mining, sentiment analysis is a text analysis technique that uses natural language processing (NPL) and machine learning algorithms to perform large scale analysis of customer conversations on social media (i.e., social media likes and/or comments, customer reviews, and shares and/or retweets) in real time. In this way, brands are able to detect social mentions and gauge the customer’s sentiments about a particular product or service and give quick and accurate responses. Sentiment analysis also allows businesses to monitor competitor presence. Based on what customers have to say about a particular product or service, competitors are able to compare their products and determine what to improve on or figure out where their competitive advantage lies.

How Does Sentiment Analysis Work?

Sentiment Analysis can be categorized into three models:

Polarity of Opinion

Polarity of opinions involves categorizing customer conversations into either positive, negative, or neutral. Alternatively, marketers can carry out a fine-grained sentiment analysis which allows them to sort data at scale. In this way customer conversations can be categorized on a scale of very positive, positive, neutral, negative, to very negative. This type of model is commonly applied to polls, surveys and reviews.

5 stars means very positive while 1 star means very negative

Personal Feelings

Personal feelings can be measured using emotion detection. For this type of model, specific emotions such as anger or happiness can be detected from text. This works by using a word list that is defined by emotions (e.g., good to mean positive emotions or bad to mean negative emotions). This however can be misinterpreted as certain words associated with negative emotions may also be used to denote happiness or satisfaction (e.g., sick, wicked, ill and mad can be used to as praise e.g., ‘That band has mad skills’). In this case, advanced machine learning algorithms are used which can detect more complex sentiments such as irony and sarcasm.

Intentions/Objectives

Intentions can be analyzed using aspect-based sentiment analysis. This involves a combination of both opinion polarity and emotion detection.

Sentiment Analysis Algorithms

There are three types of algorithms that can be applied to sentiment analysis models:

  1. Automatic
  2. Hybrid

Rule Based Algorithms

Rule based algorithms use manually crafted rules developed in computational linguistics such as lexicons (i.e., list of words and expressions)to help identify subjectivity or polarity of opinion. The system categorizes words into either negative or positive and counts the number of times each category of words appears in a text. The sentiment is positive if the appearance of positive words exceeds that of negative words, and negative if vice versa. If the appearance of both negative and positive words are similar, then the system returns a neutral sentiment.

Automatic Algorithms

This system relies on machine learning techniques where a classifier is fed a text and returns either a positive, negative, or neutral category.

Source: MonkeyLearn
Source: MonkeyLearn
Source: MonkeyLearn
  1. A measure of the presence of words

Hybrid Algorithms

This system involves the combined use of rule based and automatic algorithms. This creates more accurate results.

You can learn more about sentiment analysis with python in the following link:
https://towardsdatascience.com/sentiment-analysis-of-social-media-with-python-45268dc8f23f

Social Media and Brand Monitoring

Sentiment analysis allows brands to get instant feedback about market campaigns, new products, or services. Keeping track of real time sentiments from customers is important. This is because customer conversations are likely to include either negative mentions or praise. Whichever the case, it is important for brands to respond to customers’ sentiments as quickly as possible before things spiral out of control.

Conclusion

Social media is an indispensable and efficient tool for businesses looking to increase their customer base and expand their brand presence online. Using sentiment analysis on social media is a creative yet vital marketing strategy for businesses seeking to better understand their customers and gain marketplace insights. However, like with other marketing strategies, social media marketing has its challenges which business should carefully consider and be ready to tackle.

An Economist curious about world of Data Science