about Sentimental Analysis
Sentimental analysis is the process of identifying and detecting negative or positive emotion/ sentiment from text data. It has tremendous application in the business to detect sentiment detection in social data, anticipate brand reputation, and understand customers. Analyzing posts, comments on social platforms is a normal practice to monitor and understand the sentiment of customers.
Positive, negative, neutral, angry, happy, sad, urgent, not urgent, having interest or not are some of the sentiments which are generally analyze. The intent analysis is a game-changer for analyzing and understanding users’ intent behind the messages and identify opinions, news, reputation, suggestions, query, or issues. so, Many emotion detection systems use lists of predefined or pre-processed words that define certain emotions.
This type of system is often known as Bag of Words or lexicon-based sentiment analysis model. Sentiment analysis allows businesses to make sense by processing, tagging, and understanding it with minimal or no human interference.
Example of how the systems works
- Define 2 datasets, namely “negative_words” and “positive_words” (negative words such as Angry, Anguish, Animosity, Annihilate etc and positive words such as amazing, beneficiary, benefit, excel, excellent, good, etc).
- Count the number of positive words and negative words that appear in a given text.
- Create a table consisting of the count of total positive and negative words.
- Tally the numbers.
- If the number of positive words that appeared is greater than the number of negative words that appeared in the table then the statement is having positive sentiment. Similarly, if the number of negative words that appeared is greater than the number of positive words that appeared in the table then the statement is having negative sentiment. If the numbers are even or similar in range, then the system will return a neutral sentiment.
The sequence of the words is also very important which is often ignored by some algorithms. Many advanced processing methods and rule based techniques enhance the efficiency of emotion classifiers. However, adding new rules affects results from the past, making the system complex.
Example code:
Source: https://github.com/jeetbarot1998/Sentiment-Analyzer
Explanation
Python Code for Sentiment analyzer with Twitter data, having 2 other data sets of “negative_words.txt” and “positive_words.txt”. Twitter data is to be analyze with how many positive and negative words exist. So a set of Predefine negative and positive words are compile and save into “negative_words.txt” and “positive_words.txt” respectively. Total Score is Calculate based on positive and negative words. Then a graph of Net Score vs No. of Re-tweet is build in order to understand the relationship between retweet and score of the tweet sentiment.
Conclusion
Modern analytical methods of deriving meaningful insights from social media are enabling businesses and organizations to take data-driven and well-informed decisions. Decisions based on a hunch and previous notions often leads to biased and skewed results. Companies have been leveraging the power of analytical methods, AI, Deep learning, and classifiers to understand the market and business.
Emotion classifiers can be applied to multiple aspects of business, from customer service and market research to product analytics and brand monitoring. It has moved beyond technology, and will soon become an essential tool for all companies.
Written By: Jeet Barot
Reviewed By: Krishna Heroor
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