MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
نویسندگان
چکیده
This paper describes our system submitted for the Sentiment Analysis in Twitter task of SemEval-2016, and specifically for the Message Polarity Classification subtask. We used a system that combines Convolutional Neural Networks and Logistic Regression for sentiment prediction, where the former makes use of embedding features while the later utilizes various features like lexicons and dictionaries.
منابع مشابه
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that ou...
متن کاملDSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach
This paper contains the description of our participation at task 4 (sub-task A, Message Polarity Classification) of SemEval-2016. Our proposed system consists mainly of three steps. Firstly, the preprocessing step includes the tokenization and identification of special elements including URLs, hashtags, user mentions and emoticons. The second step aims at selecting and extracting the feature se...
متن کاملGTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System
This paper presents the approach of the GTI Research Group to SemEval-2016 task 4 on Sentiment Analysis in Twitter, or more specifically, subtasks A (Message Polarity Classification), B (Tweet classification according to a two-point scale) and D (Tweet quantification according to a two-point scale). We followed a supervised approach based on the extraction of features by a dependency parsing-ba...
متن کاملmib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
This work presents our team solution for task 4a (Message Polarity Classification) at the SemEval 2016 challenge. Our experiments have been carried out over the Twitter dataset provided by the challenge. We follow a supervised approach, exploiting a SVM polynomial kernel classifier trained with the challenge data. The classifier takes as input advanced NLP features. This paper details the featu...
متن کاملAKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews
This paper describes the polarity classification system designed for participation in SemEval2016 Task 5 ABSA. The aim is to determine the sentiment polarity expressed towards certain aspect within a consumer review. Our system is based on supervised learning using Support Vector Machine (SVM). We use standard features for basic classification model. On top this, we include rules to check prece...
متن کامل