Opinion mining and Sentiment analysis have emerged as a field of study since the widespread of World Wide Web and internet. Opinion refers to extraction of those lines or phrase in the raw and huge data which express an opinion. Sentiment analysis on the other hand identifies the polarity of the opinion being extracted. In this paper we propose the sentiment analysis in collaboration with opini...

Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words re...

When searching for emerging news on named entities, many users wish to find articles containing a variety of perspectives. Advances in sentiment analysis, particularly by tools that use Recurrent Neural Networks (RNNs), have made impressive gains in their accuracy handling NLP tasks such as sentiment analysis. Here we describe and implement a special type of RNN called a Long Short Term Memory ...

Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics’ feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated ...

This paper proposes a new paradigm for sentiment analysis: translation from text documents to a set of sentiment units. The techniques of deep language analysis for machine translation are applicable also to this kind of text mining task. We developed a high-precision sentiment analysis system at a low development cost, by making use of an existing transfer-based machine translation engine.

Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level analysis of texts and are able to detect only explicit expressions of sentiment. In this paper, we present an approach towards automatically detecting emotions (as underlying components of sentiment) from contexts in which no clues of sentiment appear, ...

Sentiment Analysis (SA) and machine learning techniques are collaborating to understand the attitude of text writer, implied in particular text. Although, SA is an important challenging itself, it is very important challenging in Arabic language. In this paper, we are enhancing sentiment analysis in Arabic language. Our approach had begun with special pre-processing steps. Then, we had adopted ...

Sentiment Analysis is a natural language processing task that extracts sentiment from various text forms and classifies them according to positive, negative or neutral polarity. It analyzes emotions, feelings, and the attitude of a speaker or a writer towards a context. This paper gives comparative study of various sentiment classification techniques and also discusses in detail two main catego...

The abundance of sentiment-carrying user-generated content renders automated cross-language information monitoring tools crucial for today’s businesses. In order to facilitate cross-language sentiment analysis, we propose to compare the sentiment conveyed by unstructured text across languages through universal star ratings for intended sentiment. We demonstrate that the way natural language rev...

Sentiment analysis is the computational study of people’s opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natura...