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Machine learning in automated text categorization
TLDR
This survey discusses the main approaches to text categorization that fall within the machine learning paradigm and discusses in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation. Expand
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
TLDR
This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0. Expand
SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining
TLDR
SENTIWORDNET is a lexical resource in which each WORDNET synset is associated to three numerical scores Obj, Pos and Neg, describing how objective, positive, and negative the terms contained in the synset are. Expand
SemEval-2016 Task 4: Sentiment Analysis in Twitter
TLDR
The fourth year of the SemEval-2016 Task 4 comprises five subtasks, three of which represent a significant departure from previous editions, and the task continues to be very popular, attracting a total of 43 teams. Expand
SemEval-2013 Task 2: Sentiment Analysis in Twitter
TLDR
Crowdourcing on Amazon Mechanical Turk was used to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks, which included two subtasks: A, an expression-level subtask, and B, a message level subtask. Expand
Supervised term weighting for automated text categorization
TLDR
It is proposed that learning from training data should also affect phase (ii), i.e. that information on the membership of training documents to categories be used to determine term weights, and is called supervised term weighting (STW). Expand
Determining the semantic orientation of terms through gloss classification
TLDR
This paper presents a new method for determining the orientation of subjective terms based on the quantitative analysis of the glosses of such terms given in on-line dictionaries, and on the use of the resulting term representations for semi-supervised term classification. Expand
Determining Term Subjectivity and Term Orientation for Opinion Mining
TLDR
The task of deciding whether a given term has a positive connotations, or a negative connotation, or has no subjective connotation at all is confronted, and it is shown that determining subjectivity and orientation is a much harder problem than determining orientation alone. Expand
Evaluation Measures for Ordinal Regression
TLDR
This work proposes a simple way to turn standard measures for OR into ones robust to imbalance, and shows that, once used on balanced datasets, the two versions of each measure coincide, and argues that these measures should become the standard choice for OR. Expand
Experiments on the Use of Feature Selection and Negative Evidence in Automated Text Categorization
TLDR
This work proposes a novel variant, based on the exploitation of negative evidence, of the well-known k-NN method, and reports the results of systematic experimentation of these two methods performed on the standard REUTERS-21578 benchmark. Expand
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