期刊
SENSORS
卷 21, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/s21010133
关键词
sentiment analysis; NLP; language models; BERT; Italian language
资金
- Italian project IDEHA-Innovation for Data Elaboration in Heritage Areas - PON Ricerca e Innovazione
This translation work presents a new method for sentiment analysis on Twitter, where tweet jargon is transformed into plain text and classified using the pre-trained language model BERT. Results show effectiveness in Italian and potential applicability to other languages.
Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages.
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