Enable Machines to Feel: Sentiment Analysis

Adani Institute of Digital Technology Management (AIDTM)

Mr. Utkarsh Sharma
Assistant Professor(Big Data Analytics) at Adani Institute of Digital Technology Management

Have you ever got a text from someone and couldn’t tell if they were kidding or not? Unless we clearly tell the person how we feel, emotions don’t carry well over text, which often makes it difficult to determine the intent of the communication. When someone texts you with a sarcastic statement (without emojis) can you tell if it’s sarcastic? If they happy, annoyed, or neutral?

What is sentiment analysis?

Sentiment analysis, also known as opinion mining, or emotion AI, boils down to one thing:

It’s the process of analyzing online pieces of writing to determine the emotional tone they carry, whether they’re positive, negative, or neutral.

In simple words, sentiment analysis helps to find the author’s attitude towards a topic.

With the advent of social networks and digital marketing, customers’ opinions about products and brands have become increasingly visible. User feedback online, such as reviews, social media comments, and surveys, contains tons of valuable data. This information may provide insight into what customers think about your product, what they like and dislike, and, most importantly, how to react to their feedback. Sentiment analysis can shed more light on these topics and become a helpful tool to analyze the moods and opinions of your clients.

What is sentiment analysis used for?

Brand reputation management
Customer feedback analysis
Market research analysis
Political policymaking
Segment Buyer Groups Based on Opinions

How is Sentiment Analysis Done?

The basis of sentiment analysis is machine learning(of course we need machines to understand something then we need machine learning) and natural language processing. The basic task of any sentiment analysis algorithm is to identify whether a text belongs to the positive, negative, or neutral sentiment class. If you seek to make your sentiment analysis as precise as possible, you can add additional polarity categories, such as: Very negative, Negative, Neutral, Positive, very positive. Following are the main approaches for sentiment analysis:

Machine Learning-Based: This type of algorithm works based on training the algorithm based on the training dataset consisting of text labeled as positive, negative, or neutral. Based on the tokens in each of the texts in the training dataset, the algorithm will create a rule to identify the sentiment of future texts.
Lexicon-Based: This algorithm is based on manually created lexicons that define positive and negative strings of words. The algorithm then analyzes the amounts of positive and negative words to see which ones dominate.
Hybrid: The combination of machine learning and lexicon-based approaches to address Sentiment Analysis is called Hybrid.

You can use some popular sentiment analysis tools to take a feel how sentiment analysis works, below is the list provided for your reference:

HubSpot’s Service Hub
Quick Search
Repustate
Lexalytics
Critical Mention
Brandwatch
Social Mention
Sentiment Analyzer
Brand24

Enable Machines to Feel: Sentiment Analysis

Adani Institute of Digital Technology Management (AIDTM)

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