International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 08 | Aug 2022
p-ISSN: 2395-0072
www.irjet.net
Emotion Recognition By Textual Tweets Using Machine Learning Vijaykumar G1 Dept of MCA, Vidya Vikas Institute of Engineering And Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------users feel about each political party, its leaders, and its Abstract - Opinion mining has become difficult due of the abundance of user-generated content on social media. Twitter is used to gather opinions about products, trends, and politics as a microblogging site.
actions.
Sentiment analysis is a technique used to examine people's attitudes, feelings, and opinions toward anything. It may be applied to tweets to examine how the public feels about news, legislation, social movements, and political figures. Natural language processing and machine learning are both regarded as having a category called sentiment analysis. It is used to separate, identify, or represent views from various information structures, such as news, audits, and articles, and it classifies them as positive, neutral, or negative. From tweets in several Indian languages, election results are tough to forecast.
The project's primary goal is to predict India's five national political parties. To do this, we combined supervised and unsupervised methods. We built our classifier using Naive Bayes, a decision tree NRC Lexicon, and the SVM method, and we categorized the test data as positive, negative, neutral, and eight other categories of emotions.
To get tweets in Hindi, we used the Twitter Archiver programme. We used data (text) mining to examine 48,276 tweets that mentioned five national political parties in India over the course of a period of time. Both supervised and unsupervised methods were applied.
1.2 Scope
1.1 Objectives
➢ Building an approach that will be used to predict election outcomes using emotions. ➢ Creating a framework that is simple to use.
Opinion mining becomes challenging since there is so much customer content on social media. Twitter is used to gather opinions about customers, trending products, and political opinion as a microblogging site. Sentiment analysis is a method for examining people's attitudes, feelings, and opinions toward various topics. It may be applied to tweets to examine how the public feels about various topics, including news, policy, social movements, and individuals.
Key Words: Naive Bayes, SVM, Decision Tree, Long Short-Term Memory, and NRC Lexicon Emotion.
1. INTRODUCTION
2. Existing System
In today's environment, text or opinion mining is useful for gauging public opinion of recently released goods, such as movies, songs, books, and other media. It also distinguished between recommendations and opinions that were good, negative, and neutral. The general people are now accustomed to posting their feelings about the political leader on social media. In order to learn about the political leaders' opinions and engage the public through TV programmes, YouTube, etc., many reporters have conducted interviews with them.
Corporations are motivated by sentiment analysis to identify consumer preferences for brands, products, and services. Additionally, it is crucial in evaluating data on businesses and sectors to keep them in mind when conducting entity reviews. By extracting a large number of tweets with the aid of prototypes, Sarlan et al. He built a sentiment analysis, and the results categorized customers' thoughts expressed in tweets into negative and positive categories. They separated their research into two parts. The primary section is depend on a literature review and uses current methodologies and methods for sentiment analysis. The second section describes the operations and requirements of the application before it is developed.
The effort of using surveys and polls to research people's opinions is very time- consuming and expensive. Sentiment analysis is a type of data mining technique that employs NLP to determine the prevailing opinion. It is the practice of categorizing viewpoints into three groups, such as "positive," "negative," and "neutral." This data quantifies public reactions to certain people, organizations, and political discourses, showing the environmental orientation of the data. Consequently, based on social media tweets, our goal is to examine how online
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Impact Factor value: 7.529
Disadvantages ➢ Extraction keyword is improper. ➢ POS tagging is not incorporated for calculation of tweet weights.
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