Study of Twitter Sentiment Analysis using Machine Learning Algorithms on Python
17 May 2017-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 165, Iss: 9, pp 29-34
TL;DR: This paper aims to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach.
Abstract: Twitter is a platform widely used by people to express their opinions and display sentiments on different occasions. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In the past decades, the research in this field has consistently grown. The reason behind this is the challenging format of the tweets which makes the processing difficult. The tweet format is very small which generates a whole new dimension of problems like use of slang, abbreviations etc. In this paper, we aim to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach.
Citations
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29 Mar 2018
TL;DR: This paper shows sentiment analysis types and techniques used to perform extraction of sentiment from tweets, and comparative study of different techniques and approaches having twitter as a data.
Abstract: Social networking sites like twitter have millions of people share their thoughts day by day as tweets. As tweet is characteristic short and basic way of expression. So in this review paper we focused on sentiment analysis of Twitter data. The Sentiment Analysis sees as area of text data mining and NLP. The research of sentiment analysis of Twitter data can be performed in different aspects. This paper shows sentiment analysis types and techniques used to perform extraction of sentiment from tweets. In this survey paper, we have taken comparative study of different techniques and approaches of sentiment analysis having twitter as a data.
60 citations
Journal Article•
TL;DR: This paper shows aftereffect of examination by utilizing different ML and Lexicon investigation methodologies to help the future investigators with understanding present beginnings in the configuration of possibility examination.
Abstract: The Sentiment Analysis is sometimes a technique to look at the information that is the form of text and determine opinions content from the text. It is also termed as emotion or feeling mining. On-line communication channels like Twitter, Facebook, YouTube, and so forth are these days a lot of passion into human life. People share their thoughts or feelings thereon. During this review paper, we tend to match on opinion mining or feeling assessment which is an area of web data mining and Machine Learning. This paper shows aftereffect of examination by utilizing different ML and Lexicon investigation methodologies. Outcomes are analyzed to play out an evaluation study and check the estimation of the present composition. In this manner, it will help the future investigators with understanding present beginnings in the configuration of possibility examination.
40 citations
Cites methods from "Study of Twitter Sentiment Analysis..."
...In [23], research works shows that different machine learning methods are used to extract the emotions....
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27 Jul 2019
TL;DR: This research paper will focus on techniques of sentiment analysis where it will perform how to extract tweets from twitter and eventually it will compare different sentiment analysis techniques and also the approaches containing twitter dataset.
Abstract: Twitter is the popular micro blogging site where thousands of people exchange their thoughts daily in the form of tweets. The characteristics of tweet is to be short and simple way of expressions. Though this paper will focus on sentiment analysis of twitter data. The research area of sentiment analysis are text data mining and NLP. In different form we can perform the sentiment analysis on twitter data. This research paper will focus on techniques of sentiment analysis where we will perform how to extract tweets from twitter. Eventually we will compare different sentiment analysis techniques and also the approaches containing twitter dataset.
37 citations
01 Dec 2020
35 citations
01 Feb 2020
TL;DR: Textblob, Sentiwordnet, MNB, LR, SVM and RNN Classifier are applied on the above dataset and a comparison is drawn between the results obtained, classifying tweets according to the sentiment expressed in them, i.e. positive or negative.
Abstract: Sentiment analysis is a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Classifiers used are of mainly two types, namely lexicon-based and machine learning based. The former include SentiWordNet and Word Sense Disambiguation while the latter include Multinomial Naive Bayes(MNB), Logistic Regression(LR), Support Vector Machine(SVM) and RNN Classifier. In this paper, existing datasets have been used, the first one from "Sentiment140" from Stanford University, consisting of 1.6 million tweets and the other one originally came from "Crowdflower’s Data for Everyone library", consisting of 13870 entries, and both datasets are already categorised as per the sentiments expressed in them. Textblob, Sentiwordnet, MNB, LR, SVM and RNN Classifier are applied on the above dataset and a comparison is drawn between the results obtained from above mentioned sentiment classifiers, classifying tweets according to the sentiment expressed in them, i.e. positive or negative. Also, along with the machine learning approaches, an ensemble form of MNB, LR and SVM has been performed on the datasets and compared with the above results. Further the above trained models can be used for sentiment prediction of a new data.
20 citations
References
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23 Jun 2011
TL;DR: This article introduced POS-specific prior polarity features and explored the use of a tree kernel to obviate the need for tedious feature engineering for sentiment analysis on Twitter data, which outperformed the state-of-the-art baseline.
Abstract: 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 for tedious feature engineering. The new features (in conjunction with previously proposed features) and the tree kernel perform approximately at the same level, both outperforming the state-of-the-art baseline.
1,652 citations
04 Jul 2013
TL;DR: A new feature vector is presented for classifying the tweets as positive, negative and extract peoples' opinion about products using Machine Learning approach.
Abstract: Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in source text. Social media is generating a vast amount of sentiment rich data in the form of tweets, status updates, blog posts etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the crowd. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters that are allowed in Twitter is 140. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, we try to analyze the twitter posts about electronic products like mobiles, laptops etc using Machine Learning approach. By doing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. We present a new feature vector for classifying the tweets as positive, negative and extract peoples' opinion about products.
354 citations
01 Mar 2016
TL;DR: A positive or negative sentiment on Twitter posts is provided using a well-known machine learning method for text categorization using manually labeled (positive/negative) tweets to build a trained method to accomplish a task.
Abstract: Twitter, as a social media is a very popular way of expressing opinions and interacting with other people in the online world. When taken in aggregation tweets can provide a reflection of public sentiment towards events. In this paper, we provide a positive or negative sentiment on Twitter posts using a well-known machine learning method for text categorization. In addition, we use manually labeled (positive/negative) tweets to build a trained method to accomplish a task. The task is looking for a correlation between twitter sentiment and events that have occurred. The trained model is based on the Bayesian Logistic Regression (BLR) classification method. We used external lexicons to detect subjective or objective tweets, added Unigram and Bigram features and used TF-IDF (Term Frequency-Inverse Document Frequency) to filter out the features. Using the FIFA World Cup 2014 as our case study, we used Twitter Streaming API and some of the official world cup hashtags to mine, filter and process tweets, in order to analyze the reflection of public sentiment towards unexpected events. The same approach, can be used as a basis for predicting future events.
84 citations
05 Jan 2016
TL;DR: The proposed approach to brand-related Twitter sentiment analysis using feature engineering and the DAN2 outperforms state-of-the-art systems in both three-class and five-class tweet sentiment classification by wide margins, with classification accuracies above 80% and excellent recall of mild sentiment tweets.
Abstract: We present an approach to brand-related Twitter sentiment analysis using feature engineering and the Dynamic Architecture for Artificial Neural Networks (DAN2). The approach addresses challenges associated with the unique characteristics of the Twitter language, and the recall of mild sentiment expressions that are of interest to brand management practitioners. We demonstrate the effectiveness of the approach on a Starbucks brand-related Twitter data set. The feature engineering produced a final tweet feature representation consisting of only seven dimensions, with greater feature density. Two sets of experiments were conducted in three-class and five-class tweet sentiment classification. We compare the proposed approach to the performances of two state-of-the-art Twitter sentiment analysis systems from the academic and commercial domains. The results indicate that the approach outperforms these state-of-the-art systems in both three-class and five-class tweet sentiment classification by wide margins, with classification accuracies above 80% and excellent recall of mild sentiment tweets.
69 citations
22 May 2016
TL;DR: A pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets) and proves to be very accurate in binary classification and ternary classification.
Abstract: Most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented towards the classification of texts into positive and negative. In this paper, we propose a pattern-based approach that goes deeper in the classification of texts collected from Twitter (i.e., tweets). We classify the tweets into 7 different classes; however the approach can be run to classify into more classes. Experiments show that our approach reaches an accuracy of classification equal to 56.9% and a precision level of sentimental tweets (other than neutral and sarcastic) equal to 72.58%. Nevertheless, the approach proves to be very accurate in binary classification (i.e., classification into “positive” and “negative”) and ternary classification (i.e., classification into “positive”, “negative” and “neutral”): in the former case, we reach an accuracy of 87.5% for the same dataset used after removing neutral tweets, and in the latter case, we reached an accuracy of classification of 83.0%.
40 citations