Sentiment analysis, also known as opinion mining, is an area that analyzes people’s opinions, sentiments, evaluations, towards entities such as products, services, individuals made in blog posts, comments, reviews or tweets. In the real world, businesses and organizations always want to consider consumer or public opinion about their products and services. So sentiment classification is an impo...

Learning sentiment models from short texts such as tweets is a notoriously challenging problem due to very strong noise and data sparsity. This paper presents a novel, collaborative filtering-based approach for sentiment prediction in twitter conversation threads. Given a set of sentiment holders and sentiment targets, we assume we know the true sentiments for a small fraction of holder-target ...

English. The paper describes our submission to the task 2 of SENTIment POLarity Classification in Italian Tweets at Evalita 2016. Our approach is based on a convolutional neural network that exploits both word embeddings and Sentiment Specific word embeddings. We also experimented a model trained with a distant supervised corpus. Our submission with Sentiment Specific word embeddings achieved t...

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 ...

Traditional sentiment feature extraction methods in documentlevel sentiment classification either count the frequencies of sentiment words as features, or the frequencies of modified and unmodified instances of each of these words. However, these methods do not represent the sentiment words’ linguistic context efficiently. We propose a novel method and feature set to handle the contextual polar...

The discipline where sentiment / opinion / emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using their cognitive knowledge. Till date, all research efforts found...

Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society's 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colo...

As virtual utterances of opinions or sentiment are becoming increasingly abundant on the Web, automated ways of analyzing sentiment in such data are becoming more and more urgent. In this paper, we provide a classification scheme for existing approaches to document sentiment analysis. As the role of negations in sentiment analysis has been explored only to a limited extent, we additionally inve...

Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorit...

This paper explores whether investor sentiment has an adverse impact on corporate investment decisions and whether such impact, if present, can be effectively mitigated by sound corporate governance mechanisms. The sample comprises listed firms in Taiwan between 2003 and 2010. Empirical results indicate that investor sentiment is significantly and positively related to amount of new investment ...