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Sentiment Analysis Through Finite State Automata

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Book cover Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13452))

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Abstract

The present research aims to demonstrate how powerful Finite State Automata (FSA) can be, into a domain in which the vagueness of the human opinions and the subjectivity of the user generated contents make the automatic “understanding” of texts extremely hard. Assuming that the semantic orientation of sentences is based on the manipulation of sentiment words, we built from scratch, for the Italian language, a network of local grammars for the annotation of sentiment expressions and electronic dictionaries for the classification of more than 15,000 opinionated words. In the paper we explain in detail how we made use of FSA for both the automatic population of sentiment lexicons and the sentiment classification of real sentences.

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Notes

  1. 1.

    Annibale Elia, Lorenza Melillo and Alessandro Maisto worked on the Conclusion of the paper, while Serena Pelosi on Introduction and Paragraphs 1, 2, 3 and 4.

  2. 2.

    We chose a rule-based method, among others, in order to verify the hypothesis that words can be classified together in accordance to both semantic and syntactic criteria.

  3. 3.

    Word Similarity is a very frequently used method in the dictionary propagation over the thesaurus-based approaches. Examples are the Maryland dictionary, created thanks to a Roget-like thesaurus and a handful of affixe [48], and other lexicons based on WordNet, like SentiWordNet, built on the base of quantitative analysis of glosses associated to synsets [17, 18] or other lexicons based on the computing of the distance measure on WordNet [17, 34].

  4. 4.

    Seed words are words which are strongly associated with a positive/negative meaning, such as eccellente (“excellent”) or orrendo (“horrible”), by which it is possible to build a bigger lexicon, detecting other words that frequently occur alongside them.

  5. 5.

    http://valeriobasile.github.io/twita/downloads.html.

  6. 6.

    https://www.celi.it/.

  7. 7.

    http://ai-applied.nl/sentiment-analysis-api.

  8. 8.

    http://hdl.handle.net/20.500.11752/ILC-73.

  9. 9.

    Although WordNet does not include semantic orientation information for its lemmas; semantic relations, such as synonymy or antonymy, are commonly used in order to automatically propagate the polarity, starting from a manually annotated set of seed word. [2, 13, 18, 18, 28, 31, 31, 34, 39, 45, 45]. This approach presents some drawbacks, such as the lack of scalability, the unavailability of enough resources for many languages and the difficulty to handle newly coined words, which are not already contained in the thesauri.

  10. 10.

    Morphemes allow not only the propagation of a given word polarity (e.g. en-, -ous, -fy), but also its switching (e.g. dis-, -less), its intensification (e.g. super-, over-) and its weakening (e.g. semi-) [54].

  11. 11.

    While compiling the dictionary, the judgment on the words “prior polarity” is given without considering any textual context. The entries of the sentiment dictionary receive the same annotation and, then, are grouped together if they posses the same semantic orientation. The Prior Polarity [56] refers to the individual words Semantic Orientation (SO) and differs from the SO because it is always independent from the context.

  12. 12.

    Local grammars are algorithms that, through grammatical, morphological and lexical instructions, are used to formalize linguistic phenomena and to -parse texts. They are defined “local” because, despite any generalization, they can be used only in the description and analysis of limited linguistic phenomena.

  13. 13.

    The main difference between the words listed in the two scales is the possibility to use them as indicators for the subjectivity detection: basically, the words belonging to the evaluation scale are “anchors” that begin the identification of polarized phrases or sentences, while the ones belonging to the strength scale are just used as intensity modifiers (see Paragraph 5.3).

  14. 14.

    available for consultation at http://dsc.unisa.it/composti/tavole/combo/tavole.asp.

  15. 15.

    The morphological method could be also applied to Italian verbs, but we chose to avoid this solution because of the complexity of their argument structures. We decided, instead, to manually evaluate all the verbs described in the Italian Lexicon-grammar binary tables, so we could preserve the different lexical, syntactic and transformational rules connected to each one of them [16].

  16. 16.

    The meaning of the deadjectival adverbs in -mente is not always predictable starting from the base adjectives from which they are derived. Also the syntactic structures in which they occur influences their interpretation. Depending on their position in sentences, the deadjectival adverbs can be described as adjective modifiers (e.g. altamente “highly”), predicate modifiers (e.g. perfettamente “perfectly”) or sentence modifiers (e.g. ultimamente “lately”).

  17. 17.

    Metanodes are labeled through the six corresponding values of the evaluation scale, which goes from –3 to +3.

  18. 18.

    The dataset contains Italian opinionated texts in the form of users reviews and comments from e-commerce and opinion websites; it lists 600 texts units (50 positive and 50 negative for each product class) and refers to six different domains, for all of which different websites (such as www.ciao.it; www.amazon.it; www.mymovies.it; www.tripadvisor.it) have been exploited [44].

  19. 19.

    Other idioms included in our resources are of the kind N0 essere (Agg + Ppass) Prep C1 (e.g. Max è matto da legare, “Max is so crazy he should be locked up”); N0 essere Agg e Agg (e.g. Max è bello e fritto, “Max is cooked”); C0 essere Agg (come C1 + E) (e.g. Mary ha la coscienza sporca \(\leftrightarrow \) La coscienza è sporca, “Mary has a guilty conscience” \(\leftrightarrow \) “The conscience is guilty”), N0 essere C1 Agg (e.g. Mary è una gatta morta, “Mary is a cock tease”).

  20. 20.

    Words that, at first glance, seem to be intensifiers but at a deeper analysis reveal a more complex behavior are abbastanza “enough” troppo “too much” and poco “not much”.

    In this research we noticed as well that the co-occurrence of troppo, poco and abbastanza with polar lexical items can provoke, in their semantic orientation, effects that can be associated to other contextual valence shifters. The ad hoc rules dedicated to these words (see Table ??) are not actually new, but refer to other contextual valence shifting rules that have been discussed in this Paragraph.

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Authors and Affiliations

  1. Department of Political and Communication Science, University of Salerno, Salerno, Italy

    Serena Pelosi, Alessandro Maisto, Lorenza Melillo & Annibale Elia

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    Alexander Gelbukh

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Pelosi, S., Maisto, A., Melillo, L., Elia, A. (2023). Sentiment Analysis Through Finite State Automata. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_14

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