Sentiment Analysis of Twitter Data Using Naïve Bayes Classifier

Abstract

Nowadays social networking is creating a rush of users' data. Since Microblogging sites like Facebook, Twitter, etc. provides an easyand short way to express and share their view of millions of users daily. With increase of use of the microblogs people express their opinions on variety of topics, companies, products etc. These helps a lot for the company to propagate their business by analyzing people's opinion. The intention of this paper is to develop a model that analyzes the sentiment behind the tweets gathered from the Twitter. The challenge is the data that are there in Twitter are extremely unformed which makes difficult to study. In our model, the sentiment analysis process is as follows: (1) Collect tweets directly using Twitter API, (2) Data Cleaning, (3) Training the data into our model i.e. Naïve Bayes, (4) Extracted tweets are classified by its sentiment i.e. its polarity (positive, negative or neutral). In our work we have used a supervised machine learning algorithm i.e. Naïve Bayes Classifier.

A dynamic weight function based BERT auto encoder for sentiment analysis

“…The overview, manual reading, and analyses of subjective data (sentiments, opinions) in large texts are difficult and are also expensive and time-consuming processes. Due to this, NLP in Text data is used to extract and analyze the opinions of the user automatically (Xu et al, 2020;Gowda et al, 2022;Jain et al, 2022;Kaushik and Bhatia, 2022). The pre-processing approach of sentimental analysis tweets with both English and Italian languages to transform the tweets by removing the noise and extracting the hidden information by using BERT-based languages (Pota et al, 2021;Dwivedi and Pathak, 2022).…”