In order to facilitate sentiment analysis of Persian text, we’ve designed and implemented an algorithm which aims to detect words with negative polarity. Currently most sentiment analysis algorithms depend mainly on polarity datasets. However, since negative prefixes in Persian are only attached to a semantically positive base (shaghagi, 2002), we have incorporated a negative prefix detection t...

This work presents an evaluation of the Brazilian Portuguese LIWC dictionary for Sentiment Analysis. This evaluation is conducted by comparison against two other sentiment resources for Portuguese language: Opinion Lexicon and SentiLex. We conducted an intrinsic and an extrinsic evaluations and show how LIWC dictionary could be used in sentiment analysis projects.

Sentiment analysis systems can benefit from the translation of sentiment information. We present a novel, graph-based approach using SimRank, a well-established graph-theoretic algorithm, to transfer sentiment information from a source language to a target language. We evaluate this method in comparison with semantic orientation using pointwise mutual information (SO-PMI), an established unsupe...

In this paper we propose a method that uses corpora where phrases are annotated as Positive, Negative, Objective and Neutral, to achieve new sentiment resources involving words dictionaries with their associated polarity. Our method was created to build sentiment words inventories based on sentisemantic evidences obtained after exploring text with annotated sentiment polarity information. Throu...

Categorical sentiment classification has drawn much attention in the field of NLP, while less work has been conducted for dimensional sentiment analysis (DSA). Recent works for DSA utilize either word embedding, knowledge base features, or bilingual language resources. In this paper, we propose our model for IJCNLP 2017 Dimensional Sentiment Analysis for Chinese Phrases shared task. Our model i...

Fine-grained sentiment analysis on the Web has received much attention in recent years. In this paper we suggest an approach to Aspect-Based Sentiment Analysis that incorporates structural information of reviews by employing Rhetorical Structure Theory. First, a novel way of determining the context of an aspect is presented, after which a full path analysis is performed on the found context tre...

Deep learning has an edge over the traditional machine learning algorithms, like SVM and Naı̈ve Bayes, for sentiment analysis because of its potential to overcome the challenges faced by sentiment analysis and handle the diversities involved, without the expensive demand for manual feature engineering. Deep learning models promise one thing given sufficient amount of data and sufficient amount o...

Twitter popularity has increasingly grown in the last few years making influence on the social, political and business aspects of life. Therefore, sentiment analysis research has put special focus on Twitter. Tweet data have many peculiarities relevant to the use of informal language, slogans, and special characters. Furthermore, training machine learning classifiers from tweets data often face...

Twitter has grown in popularity during the past decades. It is now used by millions of users who share information about their daily life and their feelings. In order to automatically process and analyze these data, applications can rely on analysis methods such as sentiment analysis and topic modeling. This paper contributes to the sentiment analysis research field. First, the preprocessing st...

This paper discusses the challenges in carrying out fair comparative evaluations of sentiment analysis systems. Firstly, these are due to differences in corpus annotation guidelines and sentiment class distribution. Secondly, different systems often make different assumptions about how to interpret certain statements, e.g. tweets with URLs. In order to study the impact of these on evaluation re...