Authors: Prof. R Y Totare, Aishwarya Ahergawli, Abhijeet Girase, Ishwari Tale, Ayushi Khanbard
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Social Media sites like twitter have billions of people share their opinions day by day as tweets. As tweet is characteristic short and basic way of human emotions. So, in this paper we focused on sentiment analysis of Twitter data. Most of Twitter\'s existing sentiment analysis solutions basically consider only the textual information of Twitter messages and strives to work well in the face of short and ambiguous Twitter messages. Recent studies show that patterns of spreading feelings on Twitter have close relationships with the polarities of Twitter messages. In this paper focus on how to combine the textual information of Twitter messages and sentiment dissemination models to get a better performance of sentiment analysis in Twitter data. To this end, proposed system first analyses the diffusion of feelings by studying a phenomenon called inversion of feelings and find some interesting properties of the reversal of feelings. Therefore, we consider the interrelations between the textual information of Twitter messages and the patterns of diffusion of feelings, and propose random forest machine learning to predict the polarities of the feelings expressed in Twitter messages. As far as we know, this work is the first to use sentiment dissemination models to improve Twitter\'s sentiment analysis. Numerous experiments in the real-world dataset show that, compared to state-of-the-art text-based analysis algorithms.
Twitter, a popular micro blogging service around the world, has shaped and transformed the way people get information from the people or organizations that interest them. On Twitter, users can post status update messages, called tweets, to tell their followers what they are thinking, what they are doing or what is happening around them. In addition, users can interact with another user by replying or republishing their tweets. Since its creation in 2008, Twitter has become one of the largest online social media platforms in the world. Given the increasing amount of data available from Twitter, the polarity of the feelings of mining users expressed in Twitter messages has become a hot research topic due to its wide applications. For example, in analysing the polarities of Twitter users on political parties and candidates, different tools have been developed to provide strategies for political elections. Commercial companies also use Twitter sentiment analysis as a quick and effective way to monitor people's feelings about their products and brands. This analysis is done by looking for opinions or sentiments from several sentences or tweets obtained. Therefore, this stack of text data in Twitter is quite valuable because it stores valuable information. To uncover this information, data mining needs to be done using certain techniques. Mining this data can be done using text mining techniques which can be combined also using the Natural Language Pre-processing approach. Furthermore, important data that has been mined needs to be determined by the type of sentiment. This is done by using analytical sentiments. Twitter is one type of social media that is often used. Users use Twitter to convey their Twitter to the general public. The number of Twitter users has reached 330 million people worldwide and every second produces 18000 data. The chirp delivered can be in the form of news, opinions, arguments, and several other types of sentences. This causes twitter to be rich in text that has certain data. In general, someone wants opinions from other people as input to determine decisions. This opinion can be done by asking directly. By asking directly, it takes time and effort to meet people who are believed to ask. Another way is to get opinions from Twitter. Opinions in the form of tweets provided by Twitter with a large amount. However, this opinion must be distinguished based on the type of positive, negative, and neutral opinions. In addition, these tweets have not been grouped according to the categories you want to find. So, it is still widespread and necessary.
II. RELATED WORK
III. EXISTING SYSTEM
Lot of work has been done in this field because of its extensive usage and applications. In this section, some of the approaches which have been implemented to achieve the same purpose are mentioned. These works are majorly differentiated by the algorithm for twitter sentiment analysis systems.
As my point of view when I studied the papers the issues are related to twitter sentiment analysis systems. Social media, such as Twitter and Facebook, users post many messages including their opinions and feelings. One of the most successful social media, Twitter, allows users to post tweets. Twitter sentiment analysis basically only considers the textual information of Twitter messages, but ignores sentiment diffusion information.
IV. PROPOSED APPROACHES
Proposed sentiment diffusion on Twitter by investigating sentiment reversal, the phenomenon that a tweet and its retweet have different sentiment polarities. We analyse the properties of sentiment reversals, and propose a sentiment reversal prediction model.
To predict the sentiment polarity of each Twitter message, we propose an iterative algorithm called SentiDiff, which takes the inter-relationships between textual information of Twitter messages and sentiment diffusion patterns into consideration. Given a tweet and its retweet, if their sentiment polarities predicted by textual information-based sentiment classifier are consistent with the prediction result of sentiment reversal, the probability of messages to be classified correctly by textual information based sentiment classifier will increase. Otherwise, the probability will decrease. In this way, sentiment reversals can be combined with textual information of Twitter messages.
Polarity of mining sentiments expressed in Twitter messages is a significant and challenging task. Most existing Twitter sentiment analysis solutions consider only the textual information of Twitter messages and cannot achieve satisfactory performance due to the unique characteristics of Twitter messages. Although recent studies have shown that patterns of feeling diffusion are closely related to the polarities of Twitter messages, existing approaches are essentially based only on textual information from Twitter messages, but ignore the dissemination of information about feelings. Inspired by the recent work on the fusion of knowledge of multiple domains, take a first step towards combining textual information and spreading feelings to geta better performance of Twitter’s sentiment analysis.
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Copyright © 2022 Prof. R Y Totare, Aishwarya Ahergawli, Abhijeet Girase, Ishwari Tale, Ayushi Khanbard. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.