Sentiment Analysis of Twitter Data

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, emotions, political and religious views from written language about personality, product or event and determined whether they are viewed positively or negatively. Our project will involve collection of data from web resources such as twitter by using Hadoop and intend to derive useful inferences and recommendations. From the evaluation of this study it can be concluded that the proposed machine learning and natural language processing techniques are an effective and practical methods for sentiment analysis

Implicit Sentiment Identification using Aspect based Opinion Mining

Opinion mining or sentiment analysis is the computational study of opinions or emotions towards aspects or things. The aspects are nothing but attributes or components of the individuals, events, topics, products and organizations. Opinion mining has been an active research area in Web mining and Natural Language Processing (NLP) in recent years. With the explosive growth of E-commerce, there are millions of product options available and people tend to review the viewpoint of others before buying a product. An aspect-based opinion mining approach helps in analyzing opinions about product features and attributes. This project is based on extracting aspects and related customer sentiments on tourism domain. This offers an approach to discover consumer preferences about tourism products and services using statistical opinion mining. The proposed system tries to extract both explicit aspects as well as implicit aspects from customer reviews. It thus increases the sentiment orientation of opinion. Most of the researches were based on explicit opinions of customers. This system tries to retrieve implicit sentiments. Due to the growing availability of unstructured reviews, the proposed system gives a summarized form of the information that is obtained from the reviews in order to furnish customers with pin point or crisp results. DOI: 10.17762/ijritcc2321-8169.16049

PERAN OPINION MINING DAN SENTIMENT ANALYSIS UNTUK MENGIDENTIFIKASIKAN SENTIMEN PUBLIK DALAM SISTEM E-GOVERNANCE

People use text to communicate their emotion and opinion about some subjects.Currently government online media provides the facility for the citizen to express their opinions through text. E-governance system that built by the government will function better if the data that contains citizen's opinion not only saved but can be managed to be more valuable information.Opinion Mining and Sentiment Analysis is the science of natural language processing that could identify emotions, sentiments and main ideas expressed in the text. The government can take advantage by embedding the opinion mining and sentiment analysis methods in the e-governance system.Citizen's opinion about the services or policies of the government is an information that required by the Government in running the good government system. In this study, will be discussed some methods of opinion mining and sentiment analysis that have been performed by some previous researchs

Analyzing Employee Voice Using Real-Time Feedback

People nowadays tend to use social media as a platform to share their reviews, emotions, and opinions. Thus, a lot of data is available on the web. Therefore, a rapid response is needed to analyse and interpret the data. Compared to other conventional datasets such as company survey and questionnaire, decision-makers could make decision effectively and efficiently by using the interpreted data. This may be done with the help of sentiment analysis method. In this research, we classify the feedback based on its category first, then each of the classified feedback is labelled based on its sentiment. Several classification algorithms are used in opinion mining, one of them is Naive Bayes Classifier. This paper aims to classify feedback based on sentiments using Naive Bayes Classifier. Keywords—Text Mining, Sentiment Analysis, Data Classification, Naive Bayes Classifier, Big Dat

Stock Prediction Analyzing Investor Sentiments

We are going through a phase of data evolution where a major portion of the data from our daily lives is now been stored on social media platforms. In recent years, social media has become ubiquitous and important for social networking and content sharing. Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. In the financial sector, sentiments are also of paramount importance, and this dissertation mainly focuses on the effect of sentiments from investors [3] on the behavior of stocks. The dissertation work leverages social data from Twitter and seeks the sentiment of certain investors. Once the sentiment of the tweets is calculated using an advanced sentiment analyzer, it is used as an additional attribute to the other fundamental properties of the stock. This dissertation demonstrates how incorporating the sentiments improves forecasting accuracy of predicting stock valuation. In addition, various experimental analysis on regression based statistical models are considered which show statistical measures to consider for effectively predicting the closing price of the stock. The Efficient Market Hypothesis (EMH) states that stock market prices are largely driven by additional information and follow a random walk pattern [7, 8, 37, 39, 41]. Though this hypothesis is widely accepted by the research community as a central paradigm governing the markets in general, several people have attempted to extract patterns in the way stock markets behave and respond to external stimuli. We test a hypothesis based on the premise of behavioral economics, that the emotions and moods of individuals basically the sentiments affect their decision-making process, thus, leading to a direct correlation between ?public sentiment? and ?market sentiment? [42, 43, 44, 45]. We first select key investors from Twitter [27, 28] whose sentiments matter and do sentiment analysis on the tweets pertaining to stock related information. Once we retrieve the sentiment for every stock, we combine this information with the other fundamental information about stocks and build different regression-based prediction models to predict their closing price

Enhancing the Sentiment Classification Accuracy of Twitter Data using Machine Learning Algorithms

Sentiment analysis or opinion mining is the study of public opinions, sentiments, attitudes, and emotions expressed in social media. This is one of the most dynamic research areas in natural language processing and text mining in current years. It is a domain that involves the finding of user sentiment, emotion and opinion within natural language text. The growing significance of sentiment analysis coincides with the increase of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative toward the item being reviewed. This research work shows the various pathways to perform a computational treatment of sentiments and opinions. The main aim of this work is to classify the sentiment of twitter data using machine learning algorithms. The sentiment classifications have been classified into two types which are emotional classification and polarity classification. This work has been carried out on polarity classification, which is used to classify the text such as positive, negative, and neutral. The polarity classification is done by using the subjectivity lexicon. After the polarity classification two machine learning algorithms are employed to enhance the accuracy of sentiment classification. In the Pre-processing phase, the tweets are preprocessed by using various techniques. Sentiment classification is the essential phase, where preprocessed tweets are taken as input to sentiment classification. The sentiment classification can be done by using subjectivity lexicon. The third phase of the proposed work is to compare and evaluate the performance of two machine learning algorithms which are Support Vector Machine and Decision tre

Combining strengths, emotions and polarities for boosting Twitter sentiment analysis

Twitter sentiment analysis or the task of automatically retrieving opinions from tweets has received an increasing interest from the web mining community. This is due to its importance in a wide range of fields such as business and politics. People express sentiments about specific topics or entities with different strengths and intensities, where these sentiments are strongly related to their personal feelings and emotions. A number of methods and lexical resources have been proposed to analyze sentiment from natural language texts, addressing different opinion dimensions. In this article, we propose an approach for boosting Twitter sentiment classification using different sentiment dimensions as meta-level features. We combine aspects such as opinion strength, emotion and polarity indicators, generated by existing sentiment analysis methods and resources. Our research shows that the combination of sentiment dimensions provides significant improvement in Twitter sentiment classification tasks such as polarity and subjectivity

Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection

Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sentiment analysis and emotion detection. The internet has provided an avenue for the public to express their opinions easily. These expressions not only contain positive or negative sentiments, it contains emotions as well. These emotions can help in social behaviour analysis, decision and policy makings for companies and the country. Emotion detection can further support other tasks such as opinion mining and early depression detection. This review provides a comprehensive analysis of the shift in recent trends from text sentiment analysis to emotion detection and the challenges in these tasks. We summarize some of the recent works in the last five years and look at the methods they used. We also look at the models of emotion classes that are generally referenced. The trend of text-based emotion detection has shifted from the early keyword-based comparisons to machine learning and deep learning algorithms that provide more flexibility to the task and better performance