Lexicon-Based Methods for Sentiment Analysis
Abstrak
We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.
Topik & Kata Kunci
Penulis (5)
Maite Taboada
Julian Brooke
Milan Tofiloski
Kimberly D. Voll
Manfred Stede
Akses Cepat
- Tahun Terbit
- 2011
- Bahasa
- en
- Total Sitasi
- 3231×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1162/COLI_a_00049
- Akses
- Open Access ✓