Naïve Bayes Classifier for Sentiment Analysis

IRJET, 2020

Keeping current status of rapidly growing technology in mind-Internet has become a promising platform for online learning, exchanging ideas and sharing opinions. Social media contain tremendous measure of the assessment information as tweets, websites, and updates on the status, posts, and so on. In this paper, the most famous micro blogging platform twitter is utilized. Twitter sentiment analysis is used for analyzing the information from Twitter (tweets), to fetch user's opinions, thoughts and sentiments. The primary objective is to investigate how text analysis strategies can be utilized to discover a part of the information in a series of posts focusing on various patterns of tweets, languages, tweets volumes on twitter.

2024 19th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP), 2024

Human emotions are often discerned through tone, facial expressions, and gestures via face-to-face interactions. However, the question arises: Can sentiment be accurately identified from unstructured text on social networks? In this study, we demonstrate that it is indeed possible. We applied four machine learning methods—Deep Learning, Decision Trees, Naive Bayes, and Support Vector Machines—in two classification scenarios: cross-validation and training/test sets enhanced by a meta-classifier. Our goal was to identify which combination of classification scenario, learning method, and preprocessing performs best in sentiment analysis. To validate our approach, we used a manually labeled corpus, forming three datasets of different sizes with varying preprocessing techniques. The results underscore the viability and effectiveness of the proposed approach and provide implications for various fields (product development, marketing, political analysis, customer service education, linguistic education).

International Journal of Computer Science and Mobile Applications, 2021

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), N...

Making use of social media for analyzing the perceptions of the masses over a product, event or a person has gained momentum in recent times. Out of a wide array of social networks, we chose Twitter for our analysis as the opinions expressed their, are concise and bear a distinctive polarity. Here, we collect the most recent tweets on users' area of interest and analyze them. The extracted tweets are then segregated as positive, negative and neutral. We do the classification in following manner: collect the tweets using Twitter API; then we process the collected tweets to convert all letters to lowercase, eliminate special characters etc. which makes the classification more efficient; the processed tweets are classified using a supervised classification technique. We make use of Naive Bayes classifier to segregate the tweets as positive, negative and neutral. We use a set of sample tweets to train the classifier. The percentage of the tweets in each category is then computed and the result is represented graphically. The result can be used further to gain an insight into the views of the people using Twitter about a particular topic that is being searched by the user. It can help corporate houses devise strategies on the basis of the popularity of their product among the masses. It may help the consumers to make informed choices based on the general sentiment expressed by the Twitter users on a product.

The president's official decision regarding the relocation of the capital city, which previously was in Jakarta and will be moved to East Kalimantan, will be a big issue and generate a lot of debate from both the agree and the disagree Indonesian citizen because this project will require a lot of funds and will have massive impacts on various sectors. This study uses 1290 tweet data consisting of 686 negative sentiments and 603 positive sentiments. These data were used as training data to create evaluation models using confusion matrix and split validation techniques. In this study, it is used TF-IDF word-weighted feature extraction with the Naive Bayes method. The result from the experiments, which was carried out on the best accuracy by splitting data 90:10 for training and testing respectively, is 76.74%. The model that has been made is implemented in 1115 test data resulting in 799 negative sentiments and 316 positive sentiments.

International Journal of Science Technology & Engineering

Microblogging websites such as twitter have evolved into source of unfettered and wide ranging category of information. Use of socially generated big data to access information about collective states of the minds in human societies becomes a new paradigm in the emerging field of computational social science. One of the natural applications of this would be prediction of the society's reaction to a new product in the sense of popularity and adoption rate. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. Using the corpus, we build a sentiment classifier that is able to determine positive, negative and neutral sentiments for a document.

As social media is maturing and growing, sentiment analysis of online communication has become a new way to gauge public opinions of events and actions in the world. Twitter has become a new social pulpit for people to quickly "tweet" or voice their ideas in a 140 characters or less. They can choose to "retweet" or share a tweet, to promote ideas that they find favorable and elect to follow others whose opinion that they value. Specifically, we studied sentiment toward tech companies in twitter. We tried several methods to classify tweets as positive, neutral, irrelevant, or negative. We were able to obtain high overall accuracy, with the caveat that the distribution of classes were skewed in our dataset.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023

Twitter sentiment analysis has gained significant attention in recent years due to the vast amount of data generated on social media platforms and the potential applications of sentiment analysis in various domains. This paper presents an overview of Twitter sentiment analysis, including its applications, challenges, and state-of-the-art techniques. We discuss the various methods and algorithms used for sentiment analysis and highlight their advantages and limitations. We also examine the ethical considerations associated with sentiment analysis and the need for responsible and transparent use of this technique. Through a comprehensive analysis of the existing literature, we explore the potential of Twitter sentiment analysis for understanding public opinion and its implications for various domains, including marketing, politics, and social sciences. Finally, we present a case study to demonstrate the application of sentiment analysis in the context of real-world data. Our analysis shows that Twitter sentiment analysis has significant potential for understanding public opinion, tracking trends, and providing valuable insights into various domains. However, it also highlights the need for careful consideration of ethical concerns and the importance of responsible and transparent use of this technique.

2016

1 Research student, Dept. of CS & IT, Dr. BAMU, Aurangabad, Maharashtra, India. 2 Professor, Dept. of CS & IT, Dr. BAMU, Aurangabad, Maharashtra, India. 3 Research student, Dept. of CS & IT, Dr. BAMU, Aurangabad, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Today microblogging website are very popular like twitter on which user post their views, opinion etc. The information is generated either through computer or mobile by one user and many can view them. In this paper we focus on using twitter for sentiment analysis. Sentiment analysis is challenging task for this we can use various machine learning algorithm for it, like Naive Bayes, SVM, maximum entropy etc. Sentiment analysis refers to predicting or telling the document or sentence text holds positive, negative or neutral opinion on some target. The aim of this paper is to revise the previous work and compa...

With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.