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SENTIMENT ANALYSIS OF ENGLISH TWEETS USING DATA MINING

SENTIMENT ANALYSIS OF ENGLISH TWEETS USING DATA MINING
Author: Dr. Gaurav Gupta
Publisher: BookRix
Total Pages: 79
Release: 2018-03-26
Genre: Technology & Engineering
ISBN: 3743852535
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Due to the popularity of internet it becomes very easy for people to share their views over social networking websites. Most popular website among them is twitter. Twitter is a widely used social networking website that is used by the numerous people to give their opinion regarding a particular topic or product. So, today it becomes necessary to analyze the tweet of the people. The process to analyze and interpret the tweets is known as sentiment analysis. The main motive of this project is to identify how the tweets on the social networking website are used to identify the opinion of people regarding the particular product or policy. Twitter is a online website that allows the user to post the status of maximum 140 characters. Twitter has over 200 million registered users and 100 million active users [34]. So it comes to be a great source of valuable information. This project aims to develop a better way for sentiment analysis which is nothing a simple way to classify the tweets into positive, negative or neutral. The result of the sentiment analysis can be used by various organizations. Sentiment analysis can be used for forecasting the stock exchange, used to predict the popularity of any product in market, or used to predict the result of elections based on the public views on the social sites. The main motive of project is to develop a better way to accurately classify the unknown tweets according to their content.

Data Mining for Tweet Sentiment Classification

Data Mining for Tweet Sentiment Classification
Author: Roy de Groot
Publisher: LAP Lambert Academic Publishing
Total Pages: 108
Release: 2012
Genre: Data mining
ISBN: 9783659295171
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The goal of this work is to classify short Twitter messages with respect to their sentiment using data mining techniques. Twitter messages, or tweets, are limited to 140 characters. This limitation makes it more difficult for people to express their sentiment and as a consequence, the classification of the sentiment will be more difficult as well. The sentiment can refer to two different types: emotions and opinions. This research is solely focused on the sentiment of opinions. These opinions can be divided into three classes: positive, neutral and negative. The tweets are then classified with an algorithm to one of those three classes. Known supervised learning algorithms as support vector machines and naive Bayes are used to create a prediction model. Before the prediction model can be created, the data has to be pre-processed from text to a fixed-length feature vector. The features consist of sentiment-words and frequently occurring words that are predictive for the sentiment. The learned model is then applied to a test set to validate the model.

Sentiment Analysis and Opinion Mining

Sentiment Analysis and Opinion Mining
Author: Bing Liu
Publisher: Morgan & Claypool Publishers
Total Pages: 185
Release: 2012
Genre: Computers
ISBN: 1608458849
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Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography

Sentiment Analysis on Twitter Data Using Machine Learning

Sentiment Analysis on Twitter Data Using Machine Learning
Author: Ravikumar Patel
Publisher:
Total Pages:
Release: 2017
Genre:
ISBN:
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In the world of social media people are more responsive towards product or certain events that are currently occurring. This response given by the user is in form of raw textual data (Semi Structured Data) in different languages and terms, which contains noise in data as well as critical information that encourage the analyst to discover knowledge and pattern from the dataset available. This is useful for decision making and taking strategic decision for the future market. To discover this unknown information from the linguistic data Natural Language Processing (NLP) and Data Mining techniques are most focused research terms used for sentiment analysis. In the derived approach the analysis on Twitter data to detect sentiment of the people throughout the world using machine learning techniques. Here the data set available for research is from Twitter for world cup Soccer 2014, held in Brazil. During this period, many people had given their opinion, emotion and attitude about the game, promotion, players. By filtering and analyzing the data using natural language processing techniques, and sentiment polarity has been calculated based on the emotion word detected in the user tweets. The data set is normalized to be used by machine learning algorithm and prepared using natural language processing techniques like Word Tokenization, Stemming and lemmatization, POS (Part of speech) Tagger, NER (Name Entity recognition) and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK), which is openly available for academic as well as for research purpose. Derived algorithm extracts emotional words using WordNet with its POS (Part-of-Speech) for the word in a sentence that has a meaning in current context, and is assigned sentiment polarity using 'SentWordNet' Dictionary or using lexicon based method. The resultant polarity assigned is further analyzed using Naïve Bayes and SVM (support vector Machine) machine learning algorithm and visualized data on WEKA platform. Finally, the goal is to compare both the results of implementation and prove the best approach for sentiment analysis on social media for semi structured data.

Twitter Data Analytics

Twitter Data Analytics
Author: Shamanth Kumar
Publisher: Springer Science & Business Media
Total Pages: 85
Release: 2013-11-11
Genre: Computers
ISBN: 1461493722
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This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter’s APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.

Text Mining with R

Text Mining with R
Author: Julia Silge
Publisher: "O'Reilly Media, Inc."
Total Pages: 193
Release: 2017-06-12
Genre: Computers
ISBN: 1491981628
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Chapter 7. Case Study : Comparing Twitter Archives; Getting the Data and Distribution of Tweets; Word Frequencies; Comparing Word Usage; Changes in Word Use; Favorites and Retweets; Summary; Chapter 8. Case Study : Mining NASA Metadata; How Data Is Organized at NASA; Wrangling and Tidying the Data; Some Initial Simple Exploration; Word Co-ocurrences and Correlations; Networks of Description and Title Words; Networks of Keywords; Calculating tf-idf for the Description Fields; What Is tf-idf for the Description Field Words?; Connecting Description Fields to Keywords; Topic Modeling.

Tweelyzer. An Approach to Sentiment Analysis of Tweets

Tweelyzer. An Approach to Sentiment Analysis of Tweets
Author: Durgesh Samariya
Publisher: Anchor Academic Publishing
Total Pages: 78
Release: 2016-10-06
Genre: Computers
ISBN: 3960675909
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The ongoing trend of people using microblogging to express their thoughts on various topics has increased the need for developing computerised techniques for automatic sentiment analysis on texts that do not exceed 200 characters. Twitter is a "micro-blogging" social networking site that has a large and rapidly growing base of users. Twitter's tweets or messages are limited to 140 characters. Because of this limitation, it is more difficult to express sentiment and the classification of the tweets is difficult as well. Sentiment analysis can be done on two types: emotion and opinion. This research completely focuses on sentiment analysis of opinions. These opinions can be divided in three different classes: positive, negative and neutral ( somewhere between positive and negative). The main goal of this study is to build a model that predicts election movement and provide sentiment score from Twitter messages (which can not exceed 140 characters). In this project, the author applies a novel approach that classifies sentiment and emotions of Twitter tweets automatically in positive, negative or neutral classes. For the sentiment, first of all, tweets from twitter were retrieved and converted into the dataset. After pre-processing the data the proposed algorithm named TWEELYZER was applied to the dataset. At the end, the performance of TWEELYZER was measured in terms of accuracy and recall. In this project, all tweets of people regarding to movies, brands, actors and actresses were collected from twitter and then cleaned and analysed according to the proposed algorithm. These tweets were collected using R Studio software. Several processes took place in pre-processing the tweets. After pre-processing the data, using R Studio led to several insights.

Text Mining on Twitter Data to Evaluate Sentiment

Text Mining on Twitter Data to Evaluate Sentiment
Author: Srijanee Niyogi
Publisher:
Total Pages: 69
Release: 2019
Genre:
ISBN:
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Social media platforms have been a major part of our daily lives. But with the freedom of expression there is no way one can check whether the posts/tweets/expressions are classified on which polarity. Since Twitter is one of the biggest social platforms for microblogging, hence the experiment was done on this platform. There are several topics that are popular over the internet like sports, politics, finance, technology are chosen as the source of the experiment. These tweets were collected over a span of time for more than 2 months via a cron job. Every tweet can be divided into three categories based on sentiment analysis, positive, negative or neutral. In the process of analyzing the sentiment, Natural Language Processing is widely used for data processing like removing stopwords, lemmatization, tokenization and POS tagging. In this work, focus is on the detection and prediction of sentiments based on tweets, associated with different topics. There are several ways to carry out the analysis using libraries, APIs, classifiers and tools. The use of data mining techniques namely data extraction, data cleaning, data storage, comparison with other reliable sources and finally sentiment analysis is followed for this thesis. In this experiments and analysis, a comparative study of sentiment analysis of various tweets collected over a span of time, by using many data mining techniques is presented. The techniques used are mainly lexicon-based, machine learning based using Random Forest Classifier, API based Stanford NLP Sentiment analyzer and a tool called SentiStrength. The fifth way of analysis is an expert, i.e. a human carrying out the analysis. In this approach, the polarity of a particular tweet is found, analyzed and a confusion matrix is prepared. From that matrix tweets are broadly classified into 4 classes, namely False Positive, False Negative, True Positive and True Negative, which are used to calculate parameters like accuracy, precision and recall. This entire task is transformed to a cloud-based web interface hosted on Amazon Web Services to carry out the operations without human intervention on live data.

Deep Learning-Based Approaches for Sentiment Analysis

Deep Learning-Based Approaches for Sentiment Analysis
Author: Basant Agarwal
Publisher: Springer Nature
Total Pages: 326
Release: 2020-01-24
Genre: Technology & Engineering
ISBN: 9811512167
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This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field.

Introduction to Data Science

Introduction to Data Science
Author: Rafael A. Irizarry
Publisher: CRC Press
Total Pages: 794
Release: 2019-11-20
Genre: Mathematics
ISBN: 1000708039
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Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.