Sentiment analysis is the computational study of people’s opinions, as expressed in text. This is an active area of research in Natural Language Processing with many applications in social media. There are two main approaches to sentiment analysis: machine learning and lexicon-based. The machine learning approach uses statistical modelling techniques, whereas the lexicon-based approach uses ‘se...

Corresponding Author: Ahmed Alsaffar Center for AI Technology, FTSM University Kebangsaan Malaysia, UKM 43000 Bangi Selangor, Malaysia Email: [email protected] Abstract: Sentiment analysis or opinion mining refers to the automatic extraction of sentiments from a natural language text. Although many studies focusing on sentiment analysis have been conducted, there remains a limited amount ...

The text documents contain opinions or sentiments on some objects, such as movie reviews, book reviews, product reviews etc. Sentiment analysis is mining the sentiment or opinion words and identification or analysis of the opinion and arguments in text. Here this paper proposed an ontology based combination approach to improve the exits approaches of sentiment classifications and to use supervi...

The paper gives an overview of the different sentiment classification approaches and tools used for sentiment analysis. Starting from this overview the paper provides a classification of (i) approaches with respect to features/techniques and advantages/limitations and (ii) tools with respect to the different techniques used for sentiment analysis. Different application fields of application of ...

Treating sentiment analysis as a classification problem has proven extremely useful, but it misses the blended, continuous nature of sentiment expression in natural language. Using data from the Experience Project, we study texts as distributions over sentiment categories. Analysis of the document collection shows the texts contain blended sentiment information substantially different from a ca...

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analy...

This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis. We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects, such as level of representation used for ...

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying presence of sentiment in text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is presence of sentiment in text, it has a s...

Twitter data has recently been considered to perform a large variety of advanced analysis. Analysis of Twitter data imposes new challenges because the data distribution is intrinsically sparse, due to a large number of messages post every day by using a wide vocabulary. Sentiment Analysis task is divided in two steps: Feature selection methods and Sentiment classification methods. Feature selec...

This report introduces the task of sentiment analysis, describes the core problems and presents the formal definition of sentiment analysis. The basic machine learning algorithms for text classification are described as well as the most commonly used features for sentiment analysis. Brief overview of distributional semantics is presented. Related work and the state-of-the-art approaches to sent...