Articles

We describe a classifier for predicting message-level sentiment of English micro- blog messages from Twitter. This paper describes our submission to the SemEval- 2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random …...

This paper describes the UNIBA team partic- ipation in the Sentiment Analysis in Twitter task (Task 10) at SemEval-2015. We propose a supervised approach relying on keyword, lex- icon and micro-blogging features as well as representation of tweets in a word space. 1...

This paper presents an overview of the sys- tem developed and submitted as a part of our participation to the SemEval-2015 Task 10 that deals with Sentiment Analysis in Twitter. We build a Support Vector Machine (SVM) based supervised learning model for Subtask A (term level task) and Subtask B …...

Like SemEval 2013 and 2014, the task Sen- timent Analysis in Twitter found a place in this year’s SemEval too and attracted an un- precedented number of participations. This task comprises of four sub-tasks. We partici- pated in subtask 2 — Message polarity classi- fication. Although we lie a few …...

We reproduce four Twitter sentiment classi- fication approaches that participated in pre- vious SemEval editions with diverse feature sets. The reproduced approaches are com- bined in an ensemble, averaging the individ- ual classifiers’ confidence scores for the three classes (positive, neutral, negative) and decid- ing sentiment polarity based on these …...

This paper describes our participation at tasks 10 (sub-task B, Message Polarity Classifica- tion) and 11 task (Sentiment Analysis of Fig- urative Language in Twitter) of Semeval2015. We describe the Support Vector Machine sys- tem we used in this competition. We also present the relevant feature set that we take …...

This paper describes our sentiment analysis systems which have been built for SemEval- 2015 Task 10 Subtask B and E. For sub- task B, a Logistic Regression classifier has been trained after extracting several groups of features including lexical, syntactic, lexicon- based, Z score and semantic features. weighting schema has …...

This paper reports our submission to task 10 (Sentiment Analysis on Tweet, SAT) (Rosen- thal et al., 2015) in SemEval 2015 , which contains five subtasks, i.e., contextual polar- ity disambiguation (subtask A: expression- level), message polarity classification (subtask B: message-level), topic-based message polar- ity classification and detecting trends towards …...

We present an approach for tackling the Sen- timent Analysis problem in SemEval 2015. The approach is based on the use of a co- occurrence graph to represent existing rela- tionships among terms in a document with the aim of using centrality measures to extract the most representative words that …...

In this paper, we propose a baseline message- level sentiment classification method, as de- veloped for SemEval-2015 Task 10, Subtask B. This system leverages both hand-crafted features and message-level embedding fea- tures, and uses an SVM classifier for message- level sentiment classification. In pre-training the embedding features, we use one …...

This paper describes our systems for expression-level and message-level sentiment analysis – two subtasks of SemEval-2015 Task 10 on sentiment analysis in Twitter. First we built two baseline systems for the two sub- tasks using SVM with a variety of features. Then we improved the systems through model iteration and …...

In this paper we present our solution for ob- taining sentiment at message-level of short sentences. The system combines the use of polarity dictionaries and Conditional Random Fields to obtain syntactic and semantic fea- tures, which are afterwards fed to a statistical classifier in order to obtain the sentence polar- …...

This paper presents the approach of the GTI Research Group to SemEval-2015 task 10 on Sentiment Analysis in Twitter, or more specif- ically, subtasks A (Contextual Polarity Disam- biguation) and B (Message Polarity Classifi- cation). We followed an unsupervised depen- dency parsing-based approach using a senti- ment lexicon, created by …...

This paper describes our automatic sentiment analysis system – CIS-positive – for SemEval 2015 Task 10 “Sentiment Analysis in Twit- ter”, subtask B “Message Polarity Classifica- tion”. In this system, we propose to normalize the Twitter data in a way that maximizes the coverage of sentiment lexicons and minimizes distracting …...

This paper describes the system that was sub- mitted to SemEval2015 Task 10: Sentiment Analysis in Twitter. We participated in Sub- task B: Message Polarity Classification. The task is a message level classification of tweets into positive, negative and neutral sentiments. Our model is primarily a supervised one which consists …...

This paper describes our sentiment classifica- tion system submitted to SemEval-2015 Task 10. In the message-level polarity classifica- tion subtask, we obtain the highest macro- averaged F1-scores on three out of six test- ing sets. Specifically, we build a two-stage classifier to predict the sentiment labels for tweets, which enables …...

We present our supervised sentiment classifi- cation system which competed in SemEval- 2015 Task 10B: Sentiment Classification in Twitter— Message Polarity Classification. Our system employs a Support Vector Ma- chine classifier trained using a number of fea- tures including n-grams, dependency parses, synset expansions, word prior polarities, and embedding clusters. …...

This paper describes our system used in the Aspect Based Sentiment Analysis Task 12 of SemEval-2015. Our system is based on two supervised machine learning algorithms: sig- moidal feedforward network to train binary classifiers for aspect category classification (Slot 1), and Conditional Random Fields to train classifiers for opinion target …...

This paper describes the SHELLFBK system that participated in SemEval 2015 Tasks 9, 10, and 11. Our system takes a supervised approach that builds on techniques from in- formation retrieval. The algorithm populates an inverted index with pseudo-documents that encode dependency parse relationships ex- tracted from the sentences in the …...

SemEval-2015 Task 12, a continuation of SemEval-2014 Task 4, aimed to foster re- search beyond sentence- or text-level senti- ment classification towards Aspect Based Sentiment Analysis. The goal is to identify opinions expressed about specific entities (e.g., laptops) and their aspects (e.g., price). The task provided manually annotated reviews in …...