Design and Evaluation of SentiEcon: a fine-grained Economic/Financial Sentiment Lexicon from a Corpus of Business News
Chantal Pérez Chantal Hernández
Proceedings of the Twelfth Language Resources and Evaluation Conference
In this paper we present, describe, and evaluate SentiEcon, a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications, for which we compiled our own corpus of online business news. SentiEcon was created as a plug-in lexicon for the sentiment analysis tool Lingmotif, and thus it follows its data structure requirements and presupposes the availability of a general-language core sentiment lexicon that covers non-specific sentiment-carrying terms and phrases. It contains 6,470 entries, both single and multi-word expressions, each with tags denoting their semantic orientation and intensity. We evaluate SentiEcon’s performance by comparing results in a sentence classification task using exclusively sentiment words as features. This sentence dataset was extracted from business news texts, and included certain key words known to recurrently convey strong semantic orientation, such as “debt”, “inflation” or “markets”. The results show that performance is significantly improved when adding SentiEcon to the general-language sentiment lexicon.
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Lingmotif: Sentiment Analysis for the Digital Humanities
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics
Lingmotif is a lexicon-based, linguistically-motivated, user-friendly, GUI-enabled, multi-platform, Sentiment Analysis desktop application. Lingmotif can perform SA on any type of input texts, regardless of their length and topic. The analysis is based on the identification of sentiment-laden words and phrases contained in the application’s rich core lexicons, and employs context rules to account for sentiment shifters. It offers easy-to-interpret visual representations of quantitative data (text polarity, sentiment intensity, sentiment profile), as well as a detailed, qualitative analysis of the text in terms of its sentiment. Lingmotif can also take user-provided plugin lexicons in order to account for domain-specific sentiment expression. Lingmotif currently analyzes English and Spanish texts.
Tecnolengua Lingmotif at EmoInt-2017: A lexicon-based approach
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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.
Managing Multiword Expressions in a Lexicon-Based Sentiment Analysis System for Spanish
Proceedings of the 9th Workshop on Multiword Expressions
New Developments in Ontological Semantics
Antonio Moreno Ortiz
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)
Reusing the Mikrokosmos Ontology for Concept-based Multilingual Terminology Databases
Proceedings of the Second International Conference on Language Resources and Evaluation (LREC’00)