Automatic Domain-Specific Sentiment Lexicon Generation with Label Propagation

Tai, Yen-Jen; Kao, Hung-Yu

doi:10.1145/2539150.2539190

Cited by 49 publications

(27 citation statements)

References 12 publications

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“…In the literature, different approaches related to the Multi-Domain sentiment analysis have been proposed. Briefly, two main categories may be identified: (i) the transfer of learned classifiers across different domains [3,[40][41][42][43][44], and (ii) the use of propagation of labels through graph structures [45][46][47][48]. Independently of the kind of approach, works using concepts rather than terms for representing different sentiments have been proposed and only recently, solutions based on the use of information retrieval strategies for building sentiment analysis systems have been presented [49].…”

Section: Resultsmentioning

confidence: 99%

Semantic Web Evaluation Challenges

Gandon

Cabrio

Stanković

et al. 2015

Communications in Computer and Information Science

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Abstract. Extracting structured information from text plays a crucial role in automatic knowledge acquisition and is at the core of any knowledge representation and reasoning system. Traditional methods rely on hand-crafted rules and are restricted by the performance of various linguistic pre-processing tools. More recent approaches rely on supervised learning of relations trained on labelled examples, which can be manually created or sometimes automatically generated (referred as distant supervision). We propose a supervised method for entity typing and alignment. We argue that a rich feature space can improve extraction accuracy and we propose to exploit Linked Open Data (LOD) for feature enrichment. Our approach is tested on task-2 of the Open Knowledge Extraction challenge, including automatic entity typing and alignment. Our approach demonstrate that by combining evidences derived from LOD (e.g. DBpedia) and conventional lexical resources (e.g. WordNet) (i) improves the accuracy of the supervised induction method and (ii) enables easy matching with the Dolce+DnS Ultra Lite ontology classes.

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Section: Resultsmentioning

confidence: 99%

Semantic Web Evaluation Challenges

Gandon

Cabrio

Stanković

et al. 2015

Communications in Computer and Information Science

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“…A number of solutions have been proposed to these, mostly using ad hoc dictionaries, sometimes created automatically from a domain-specific corpus (Tai and Kao, 2013;Lu et al, 2011).…”

Section: Lexicon-based Sentiment Analysismentioning

confidence: 99%

Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach

Ortiz¹

2017

Proceedings of the 8th Workshop on Computational Approaches To Subjectivity, Sentiment and Social Media Analysis

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In this paper we describe Tecnolengua Group's participation in the shared task on emotion intensity at WASSA 2017. We used the Lingmotif tool and a new, complementary tool, Lingmotif Learn, which we developed for this occasion. We based our intensity predictions for the four test datasets entirely on Lingmotif's TSS (text sentiment score) feature. We also developed mechanisms for dealing with the idiosyncrasies of Twitter text. Results were comparatively poor, but the experience meant a good opportunity for us to identify issues in our score calculation for short texts, a genre for which the Lingmotif tool was not originally designed.

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“…In this approach, the building lexicon is a fundamental challenge because the lexicon is used as measurement. A lot of researchers proposed unsupervised or semi-supervised approaches for building lexicon [11][12][13]. Especially, J. Bross, and H. Ehrig proposed a method for automatically adapting and extending lexicons to a specific product domain, but they simply used morphological patterns to extract aspect-based lexicon [14].…”

Section: Lexicon Building On Aspect-based Sentiment Analysismentioning

confidence: 99%