Abstract
This article describes an automated technique that allows to differentiate texts expressing a positive or a negative opinion. The basic principle is based on the observation that positive texts are statistically shorter than negative ones. From this observation of the psycholinguistic human behavior, we derive a heuristic that is employed to generate connoted lexicons with a low level of prior knowledge. The lexicon is then used to compute the level of opinion of an unknown text. Our primary motivation is to reduce the need of the human implication (domain and language) in the generation of the lexicon in order to have a process with the highest possible autonomy. The resulting adaptability would represent an advantage with free or approximate expression commonly found in social networks environment.
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Notes
- 1.
TREC (Text Retrieval Conference), NTCIR (NII Text Collection for IR), CLEF (Cross Language Evaluation Forum).
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Lancieri, L., Leprêtre, E. (2015). A New Linguistic Approach to Assess the Opinion of Users in Social Network Environments. In: Kazienko, P., Chawla, N. (eds) Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-19003-7_8
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