Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9
نویسنده
چکیده
This document describes the senti.ue system and how it was used for participation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This system maintains a supervised machine learning approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a restricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in subtask B. For the unconstrained mode, our score was slightly below, except for one case in subtask A.
منابع مشابه
Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble
We describe our approach for the SemEval-2014 task 9: Sentiment Analysis in Twitter. We make use of an ensemble learning method for sentiment classification of tweets that relies on varied features such as feature hashing, part-of-speech, and lexical features. Our system was evaluated in the Twitter message-level task.
متن کاملSWASH: A Naive Bayes Classifier for Tweet Sentiment Identification
This paper describes a sentiment classification system designed for SemEval-2015, Task 10, Subtask B. The system employs a constrained, supervised text categorization approach. Firstly, since thorough preprocessing of tweet data was shown to be effective in previous SemEval sentiment classification tasks, various preprocessessing steps were introduced to enhance the quality of lexical informati...
متن کاملsenti.ue-en: an approach for informally written short texts in SemEval-2013 Sentiment Analysis task
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 subtasks we used the same supervised machine learning approach, including two classifiers in pipeline, with 22 semantic oriented features, such as polarized term presence and index, and negation presence. Our system achieved a bette...
متن کاملNRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets
This paper describes state-of-the-art statistical systems for automatic sentiment analysis of tweets. In a Semeval-2014 shared task (Task 9), our submissions obtained highest scores in the term-level sentiment classification subtask on both the 2013 and 2014 tweets test sets. In the message-level sentiment classification task, our submissions obtained highest scores on the LiveJournal blog post...
متن کاملPUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
This paper describes a classification system that participated in SemEval-2016 Task 4: Sentiment Analysis in Twitter. The proposed approach competed in subtasks A, B, and C, which involved tweet polarity classification, tweet classification according to a two-point scale, and tweet classification according to a five-point scale. Our system is based on an ensemble consisting of Random Forests, S...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014