Sentiment Analysis of Twitter Data: A Survey of Techniques

  title={Sentiment Analysis of Twitter Data: A Survey of Techniques},
  author={Vishal.A.Kharde and Prof. Sheetal.Sonawane},
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… 
31 Citations

Approaches and Applications for Sentiment Analysis

  • M. Govindarajan
  • Computer Science
    Data Mining Approaches for Big Data and Sentiment Analysis in Social Media
  • 2022
This chapter presents sentiment analysis applications and challenges with their approaches and tools and will provide a clear-cut idea to the sentiment analysis researchers to carry out their work in this field.

Data Mining Approach of Text Classification and Clustering of Twitter Data for Business Analytics

  • P. MeghanasreeS. Gopi Krishna
  • Computer Science
    International Journal of Scientific Research in Computer Science, Engineering and Information Technology
  • 2019
A popular food brand is selected to evaluate a given stream of customer comments on Twitter to better understand the market and improve their brand.

Comparison of Web Services for Sentiment Analysis in Social Networking Sites

Four sentiment analysis web services are used which are Sentiment Analyzer, Aylien, ParallelDots, and Monkey learn and MonkeyLearn obtained the best final results among all web services with the lowest MSE score.

Topic Modeling as a Tool to Gauge Political Sentiments from Twitter Feeds

This research shows how the proposed model to predict chances of a political party winning based on data collected from Twitter microblogging website is able to examine and summarize observations based on underlying semantic structures of messages posted on Twitter.

A Review of Sentimental Analysis on Social Media Application

A brief review of work done on sentiment analysis on social media applications along with various phases and levels of sentiment analysis has been discussed.

Exploiting Chi Square Method for Sentiment Analysis of Product Reviews

This article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews and identify the feature specific intensity with which reviewer has expressed his opinion.

Exploring the Performance Characteristics of the Naïve Bayes Classifier in the Sentiment Analysis of an Airline’s Social Media Data

This study investigates the capability of the Naïve Bayes classifier for analyzing sentiments of airline image branding and examines the impact of data size on the accuracy of the classifier.

Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques

Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded. Sentiment analysis is used to analyze and predict sentiment polarities of the text.

Comparative Analysis of Sentiment Analysis Between All Bigrams and Selective Adverb/Adjective Bigrams

A comparative study of sentiment analysis performance and accuracy between all bigrams and selective adverb/adjective bigrams is done and will serve as a metric for both academia and industry to implement sentiment analysis projects.

AI-Based Learning Techniques for Sarcasm Detection of Social Media Tweets: State-of-the-Art Survey

The objective of the paper is to highlight the different types of sarcastic tweets and their usage in sentiment analysis and the use of machine learning and deep learning for identifying sarcastic tweets.



Twitter as a Corpus for Sentiment Analysis and Opinion Mining

This paper shows how to automatically collect a corpus for sentiment analysis and opinion mining purposes and builds a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document.

Opinion Mining on Social Media Data

  • Po-Wei LiangBi-Ru Dai
  • Computer Science
    2013 IEEE 14th International Conference on Mobile Data Management
  • 2013
This paper proposes a new system architecture that can automatically analyze the sentiments of microblogging messages, and combines this system with manually annotated data from Twitter, one of the most popular microblogting platforms, for the task of sentiment analysis.

Sentiment analysis in twitter using machine learning techniques

  • M. NeethuR. Rajasree
  • Computer Science
    2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)
  • 2013
A new feature vector is presented for classifying the tweets as positive, negative and extract peoples' opinion about products using Machine Learning approach.

Sentiment Analysis of User-Generated Twitter Updates using Various Classification Techniques

The problem is to determine the sentiment of a given tweet, given a user-generated status update, which would determine whether the given tweet reflects positive opinion or negative opinion on Twitter.

Sentiment Knowledge Discovery in Twitter Streaming Data

To deal with streaming unbalanced classes, a sliding window Kappa statistic is proposed for evaluation in time-changing data streams, and a study on Twitter data is performed using learning algorithms for data streams.

Domain Adaptation in Sentiment Analysis of Twitter

This paper proposes different techniques to select an out-of-domain data source that would aid in Sentiment Analysis of Twitter by adapting data from other domains, commonly referred to as Domain Adaptation.

Adaptive co-training SVM for sentiment classification on tweets

An adaptive multiclass SVM model which transfers an initial common sentiment classifier to a topic-adaptive one and achieves promising increases in accuracy averagely on the 6 topics from public tweet corpus is formally proposed.

Enhanced Sentiment Learning Using Twitter Hashtags and Smileys

A supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service, is proposed, utilizing 50 Twitter tags and 15 smileys as sentiment labels, allowing identification and classification of diverse sentiment types of short texts.

Robust Sentiment Detection on Twitter from Biased and Noisy Data

In this paper, we propose an approach to automatically detect sentiments on Twitter messages (tweets) that explores some characteristics of how tweets are written and meta-information of the words

Sentiment Analysis of Twitter Data

We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity features. (2) We explore the use of a tree kernel to obviate the need