Articles

This article describes a Sentiment Analysis (SA) system named senti.ue-en, built for participation in SemEval-2013 Task 2, a Twitter SA challenge. In both challenge sub- tasks we used the same supervised machine learning approach, including two classifiers in pipeline, with 22 semantic oriented features, such as polarized term presence and …...

We propose a method for using discourse rela- tions for polarity detection of tweets. We have focused on unstructured and noisy text like tweets on which linguistic tools like parsers and POS-taggers don’t work properly. We have showed how conjunctions, connectives, modals and conditionals affect the sentiments in tweets. We …...

We evaluate a naive machine learning ap- proach to sentiment classification focused on Twitter in the context of the sentiment analysis task of SemEval-2013. We employ a classifier based on the Random Forests al- gorithm to determine whether a tweet ex- presses overall positive, negative or neu- tral sentiment. The …...

This paper describes the system implemented by Fundaci´o Barcelona Media (FBM) for clas- sifying the polarity of opinion expressions in tweets and SMSs, and which is supported by a UIMA pipeline for rich linguistic and sen- timent annotations. FBM participated in the SEMEVAL 2013 Task 2 on polarity classifi- cation. …...

We present a supervised sentiment detection system that classifies the polarity of subjec- tive phrases as positive, negative, or neutral. It is tailored towards online genres, specifically Twitter, through the inclusion of dictionaries developed to capture vocabulary used in on- line conversations (e.g., slang and emoticons) as well as stylistic …...

In this paper we introduce our contribution to the SemEval-2013 Task 2 on “Sentiment Analysis in Twitter”. We participated in “task B”, where the objective was to build mod- els which classify tweets into three classes (positive, negative or neutral) by their con- tents. To solve this problem we basically …...

Sentiment analysis refers to automatically ex- tracting the sentiment present in a given natu- ral language text. We present our participation to the SemEval2013 competition, in the senti- ment analysis of Twitter and SMS messages. Our approach for this task is the combination of two sentiment analysis subsystems which are …...

This paper presents the Tweetsted system im- plemented for the SemEval 2013 task on Sen- timent Analysis in Twitter. In particular, we participated in Task B on Message Polar- ity Classification in the Constrained setting. The approach is based on the exploitation of various resources such as SentiWordNet and LIWC. …...

The fast development of Social Media made it possible for people to no loger remain mere spectators to the events that happen in the world, but become part of them, comment- ing on their developments and the entities in- volved, sharing their opinions and distribut- ing related content. This phenomenon …...

Sentiment Analysis in Twitter has become an important task due to the huge user-generated content published over such media. Such analysis could be useful for many domains such as Marketing, Finance, Politics, and So- cial. We propose to use many features in order to improve a trained classifier of Twitter …...

This paper describes University of Leipzig’s approach to SemEval-2013 task 2B on Sen- timent Analysis in Twitter: message polar- ity classification. Our system is designed to function as a baseline, to see what we can accomplish with well-understood and purely data-driven lexical features, simple general- izations as well as standard …...

In this paper, we describe the development and performance of the supervised system UMCC_DLSI-(SA). This system uses corpora where phrases are annotated as Positive, Negative, Objective, and Neutral, to achieve new sentiment resources involving word dictionaries with their associated polarity. As a result, new sentiment inventories are obtained and applied …...

This paper describes our submission for Se- mEval2013 Task 2: Sentiment Analysis in Twitter. For the limited data condition we use a lexicon-based model. The model uses an af- fective lexicon automatically generated from a very large corpus of raw web data. Statistics are calculated over the word and bigram …...

The paper describes experiments using grid searches over various combinations of ma- chine learning algorithms, features and pre- processing strategies in order to produce the optimal systems for sentiment classification of microblog messages. The approach is fairly domain independent, as demonstrated by the systems achieving quite competitive results when applied …...

This paper is an overview of the SwatCS system submitted to SemEval-2013 Task 2A: Contextual Polarity Disambiguation. The sen- timent of individual phrases within a tweet are labeled using a combination of classifiers trained on a range of lexical features. The classifiers are combined by estimating the ac- curacy of …...

This paper presents the contribution of our team at task 2 of SemEval 2013: Sentiment Analysis in Twitter. We submitted a con- strained run for each of the two subtasks. In the Contextual Polarity Disambiguation subtask, we use a sentiment lexicon approach combined with polarity shift detection and tree kernel …...

This paper briefly reports our submissions to the two subtasks of Semantic Analysis in Twitter task in SemEval 2013 (Task 2), i.e., the Contextual Polarity Disambiguation task (an expression-level task) and the Message Polarity Classification task (a message-level task). We extract features from surface infor- mation of tweets, i.e., content …...

This paper describes the participation of the SINAI research group in the 2013 edi- tion of the International Workshop Se- mEval. The SINAI research group has submitted two systems, which cover the two main approaches in the field of sen- timent analysis: supervised and unsuper- vised. 1...

This paper describes our approach to the SemEval-2013 task on “Sentiment Analysis in Twitter”. We use simple bag-of-words mod- els, a freely available sentiment dictionary auto- matically extended with distributionally similar terms, as well as lists of emoticons and inter- net slang abbreviations in conjunction with fast and robust machine …...

This paper describes a dual-classifier ap- proach to contextual sentiment analysis at the SemEval-2013 Task 2. Contextual analysis of polarity focuses on a word or phrase, rather than the broader task of identifying the senti- ment of an entire text. The Task 2 definition includes target word spans that range …...