TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2015

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 neutral. Several preprocessing tasks such as negation detection, spell checking, and tokenization are performed to enhance lexical information. The features are then augmented with external sentiment lexicons. Classification is done with Logistic Regression using a one-vsrest configuration. For the test runs, the system was trained using only the provided training tweets. The classifier was successful, with an F1 score of 58.43 on the official 2015 test data, and an F1 score of 66.64 on the Twitter 2014 progress data.

Section: Jazzymentioning

confidence: 99%

SWATAC: A Sentiment Analyzer using One-Vs-Rest Logistic Regression

Alhessi¹,

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2015

“…sentiment lexicons) are included, and the specifics of the classifier and its parameter settings. Many of the early modifications parroted the choices of the most successful past participants (Miura et al, 2014;Tang et al, 2014;Günther et al, 2014;Zhu et al, 2014).…”

Section: Featuresmentioning

confidence: 99%

“…After experimenting with a few options, students often chose the Jazzy 2 spell checker used by (Miura et al, 2014), though this option was largely abandoned because it produced inferior results. In particular, the dictionaries used by the spell checkers were not tailored for the colloquial, abbreviated and slangy language found Table 2: Average F1 score for systems based on the classifier used.…”

Section: Featuresmentioning

confidence: 99%

SWATCS65: Sentiment Classification Using an Ensemble of Class Projects

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2015

This paper presents the SWATCS65 ensemble 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 sentiment of an entire tweet. The ensemble was constructed from 26 classifiers, each written by a group of one to three undergraduate students in the Fall 2014 offering of a natural language processing course at Swarthmore College. Each of the classifiers was designed independently, though much of the early structure was provided by in-class lab assignments. There was high variability in the final performance of each of these classifiers, which were combined using a weighted voting scheme with weights correlated with performance using 5-fold cross-validation on the provided training data. The system performed very well, achieving an F1 score of 61.89.

“…In particular, a spell corrector, Jazzy 1 , was used as it had previously been shown to be effective (Miura et al, 2014). This step was taken to reduce dimensionality and provide better matches with the sentiment lexicon, e.g.…”

Section: Other Preprocessing Consideredmentioning

confidence: 99%

SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification

Talbot¹,

Acheampong²,

Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2015

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 information. Secondly, a Naive Bayes classifier is used to detect tweet sentiment. The classifier is trained only on the training data provided by the task organizers. The system makes use of external human-generated lists of positive and negative words at several steps throughout classification. The system produced an overall F-score of 59.26 on the official test set.