Articles

The LT3 system perceives ABSA as a task consisting of three main subtasks, which have to be tackled incrementally, namely aspect term extraction, classification and polarity classification. For the first two steps, we see that employing a hybrid terminology extrac- tion system leads to promising results, espe- cially when it …...

This paper presents our participation in SemEval-2015 task 12 (Aspect Based Senti- ment Analysis). We participated employing only unsupervised or weakly-supervised ap- proaches. Our attempt is based on requiring the minimum annotated or hand-crafted con- tent, and avoids training a model using the provided training set. We use a continuous …...

This paper describes the details of our sys- tem submitted to the SemEval 2015 shared task on sentiment analysis of figurative lan- guage on Twitter. We tackle the problem as regression task and combine several base sys- tems using stacked generalization (Wolpert, 1992). An initial analysis revealed that the data …...

The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figura- tive Language in Twitter. The proposed ap- proach employs syntactical and morphological features, which indicate sentiment polarity in both figurative and non-figurative tweets. These features were combined with others that indi- cate presence of figurative language in …...

This paper describes the system used by the ValenTo team in the Task 11, Sentiment Anal- ysis of Figurative Language in Twitter, at Se- mEval 2015. Our system used a regression model and additional external resources to as- sign polarity values. A distinctive feature of our approach is that we …...

This paper presents the system we developed for Task 11 of SemEval 2015. Our system had two stages: The first one was based on deep autoencoders for extracting features to com- pactly represent tweets. The next stage con- sisted of a classifier or a regression function for estimating the polarity …...

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We consid- ered two approaches, classification and regres- sion, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined …...

In this paper, we propose a new statistical method for sentiment analysis of figurative language within short texts collected from Twitter (called tweets) as a part of SemEval- 2015 Task 11. Particularly, the proposed model focuses on classifying the tweets into three categories (i.e., sarcastic, ironic, and metaphorical tweet) by …...

We present and evaluate several hybrid sys- tems for sentiment identification for Twit- ter, both at the phrase and document (tweet) level. Our approach has been to use a novel combination of lexica, traditional NLP and deep learning features. We also analyse tech- niques based on syntactic parsing and token- …...

In this paper, we describe our approach to Se- meval 2015 task 10 subtask B, message level sentiment detection. Our system implements a variety of classifiers and data preparation tech- niques from previous work. The set of features and classifiers used in the final system pro- duced consistently strong results …...

Microblogs are considered as We-Media infor- mation with many real-time opinions. This paper presents a Twitter-OpinMiner system for Twitter sentiment analysis evaluation at SemEval 2015. Our approach stems from two different angles: topic detection for discovering the sentiment distri- bution on different topics and sentiment analysis based on a variety …...

We present the INESC-ID system for the mes- sage polarity classification task of SemEval 2015. The proposed system does not make use of any hand-coded features or linguistic resources. It relies on projecting pre-trained structured skip-gram word embeddings into a small subspace. The word embeddings can be obtained from large …...

We describe a Twitter sentiment analysis sys- tem developed by combining a rule-based classifier with supervised learning. We sub- mitted our results for the message-level sub- task in SemEval 2015 Task 10, and achieved a F1-score of 57.06%. The rule-based classi- fier is based on rules that are dependent on …...

This paper describes SWATAC, a system built for SemEval-2015’s Task 10 Subtask B, namely the Message Polarity Classification Task. Given a tweet, the system classifies the sentiment as either positive, negative, or neu- tral. Several preprocessing tasks such as nega- tion detection, spell checking, and tokeniza- tion are performed to …...

This paper describes TwitterHawk, a system for sentiment analysis of tweets which partici- pated in the SemEval-2015 Task 10, Subtasks A through D. The system performed com- petitively, most notably placing 1st in topic- based sentiment classification (Subtask C) and ranking 4th out of 40 in identifying the sen- timent …...

This paper describes a sentiment classifica- tion system designed for SemEval-2015, Task 10, Subtask B. The system employs a con- strained, supervised text categorization ap- proach. Firstly, since thorough preprocess- ing of tweet data was shown to be effective in previous SemEval sentiment classification tasks, various preprocessessing steps were in- …...

This paper presents the SWATCS65 ensem- ble classifier used to identify the sentiment of tweets. The classifier was trained and tested using data provided by Semeval-2015, Task 10, subtask B with the goal to label the sen- timent of an entire tweet. The ensemble was constructed from 26 classifiers, each …...

This paper describes the KLUEless system which participated in the SemEval-2015 task on “Sentiment Analysis in Twitter”. This year the updated system based on the developments for the same task in 2014 (Evert et al., 2014) and 2013 (Proisl et al., 2013) participated in all five subtasks. The paper gives …...

In this paper, we describe the system we built for Task 11 of SemEval2015, which aims at i- dentifying the sentiment intensity of figurative language in tweets. We use various features, including those specially concerned with the identification of irony and sarcasm. The fea- tures are evaluated through a decision …...

We present the approach followed by INESC- ID in the SemEval 2015 Twitter Sentiment Analysis challenge, subtask E. The goal was to determine the strength of the association of Twitter terms with positive sentiment. Using two labeled lexicons, we trained a regression model to predict the sentiment polarity and in- …...