Activities per year
Abstract
In this paper we investigate methods for improving the sentiment analysis functionality of Pattern.nl, the Dutch submodule of Pattern, an open-source library for web mining and natural language processing. We discuss the impact on performance of three different potential improvements: extending the module’s internal sentiment lexicon; removing subsets of neutral words from the sentiment lexicon; and improving the algorithm for combining multiple word-level sentiment ratings into a sentence-level sentiment rating. We evaluated the improvements on datasets from the product review domain (books, clothing and music) and a dataset of short emotional stories. The experiments show that lexicon expansion does not lead to better results; new normalization techniques, on the other hand, show a limited but consistent performance increase for sentiment ratings.
Original language | English |
---|---|
Pages (from-to) | 73-89 |
Number of pages | 15 |
Journal | Computational linguistics in the Netherlands journal |
Volume | 10 |
Publication status | Published - 12 Dec 2020 |
Keywords
- Sentiment Analysis
- Pattern
- natural language processing
- Dutch
- Emotion detection
- text analysis
- Text classification
- Text Mining
Fingerprint
Dive into the research topics of 'Improving Dutch sentiment analysis in Pattern'. Together they form a unique fingerprint.Activities
- 1 Participating in a conference, workshop, ...
-
30th Meeting on Computational Linguistics in The Netherlands, CLIN 2020
van Waterschoot, J. B. (Participant), Theune, M. (Participant), Gatti, L. (Participant), Velner, P. C. (Participant), Beelen, T. H. J. (Participant), van Stegeren, J. E. (Participant), de Jong, R. F. (Participant) & Truong, K. P. (Participant)
30 Jan 2020Activity: Participating in or organising an event › Participating in a conference, workshop, ...
Research output
- 1 Citations
- 1 Abstract
-
Improving Pattern.nl sentiment analysis
Gatti, L. & van Stegeren, J., Dec 2020.Research output: Contribution to conference › Abstract › Academic
Open AccessFile