Course Content

ABSTRACT

 

Analyzing sentiment is a process to identify the opinion of a text. It is also known as opinion mining or emotion Artificial Intelligence (AI). People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. This classification can be achieved using Sentiment Analysis. Sentiment analysis takes unstructured text comments about a product reviews, an event, etc., from all comments posted by different users and classifies the comments into different categories as either positive or negative or neutral opinion. This is also known as polarity classification. Sentimental analysis can be performed by Text analysis and computational linguistics. This work aims at comparing the performance of different machine learning algorithms in performing sentiment analysis of Twitter data.

 

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression and Naïve Bayes to predict the Tweet is Positive or Negative based on Tweet entered by the user in the front end.

 

 

INTRODUCTION

Analyzing sentiment is a process to find out the opinion of a text. People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. Analyzing sentiment is a process of knowing users emotions for a particular item which may be an event or topic or individual of recent trends. Sentiment analysis can be done at three levels and they are sentence, aspect and document level. Twitter is a highly rich source of information for deciding the quality of any product. Twitter platform uses tweets which is in sentence form to denote opinions. Therefore, sentiment analysis at sentence level is used for examining sentiments. Sentiment analysis over Twitter provides the companies an effective way to find the opinions of people towards their newly launched products. The goal is to calculate the sentiment accuracy of sentences that were extracted from the text of tweets. Sentiment analysis can be done for twitter data which will classify the tweet as either positive or negative. This analysis helps concerned organizations to find opinions of people about their product, events, so on from the tweets. The opinion words are the most challenging part in sentiment analysis. An opinion word may be positive or negative depending on the situation. The meaning of the content will not be altered by traditional text processing systems when there is a little change in the words. But sentiment analysis can change the meaning of the content when there are changes in two words. For example, “The phone is ringing” is different from “the phone is not ringing”. The processing is done sentence by sentence. The informal sentence in twitter can be understood by the user whereas the system cannot understand.

 

So we proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression and Naïve Bayes to predict the Tweet is Positive or Negative based on Tweet entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Objective

The main aim of this project is to predict sentiment of the tweet using machine learning techniques, NLP techniques and machine learning algorithms like logistic regression and naïve bayes with good accuracy based on tweet entered by the user in the front end.

 

 

 

 

 

 

 

 

 

 

 

 

 

Problem Statement

 

Analyzing sentiment is a process to identify the opinion of a text. People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. Classification of whether a tweet is positive or negative plays an import role when consider for tweets about a person or event or product.

 

 

 

 

 

 

 

 

 

 

 

 

Proposed System:

 

We proposed a system with the help of machine learning techniques, NLP techniques like count vectorizer, TF-IDF Transformer and Machine learning algorithms like Logistic Regression and Naïve Bayes to predict the Tweet is Positive or Negative based on Tweet entered by the user in the front end.