аннотация8 (Аннотации)

Polyakov P. Yu., Kalinina M. V., Pleshko V. V.:Automatic Object-oriented Sentiment Analysis by Means of Semantictemplates and Sentiment Lexicon Dictionaries.The article shows the use of a linguistics-based approach to automatic objectoriented sentiment analysis. The original task was to extract users’ opinions(positive, negative, neutral) about telecom companies, expressed in tweets and news.This problem becomes popular nowadays, because many commercial companiesproducing goods and services are interested in monitoring social networkingwebsites and blogs for users’ opinions about their products and services.The authors excluded news from the dataset because they assume that formaltexts differ significantly from informal ones in structure and vocabulary andtherefore demand a different approach.

They were interested in using only thelinguistic approach based on syntactic and semantic analysis to evaluate purelinguistic approach without any machine-learning algorithm. In this approach, asentiment-bearing word or expression is linked to its target object at either of twostages, which perform successively. The first stage includes the usage of semantictemplates matching the dependence tree, and the second stage involves heuristics forlinking sentiment expressions and their target objects when syntactic relationsbetween them do not exist. The method has showed a very high effectiveness, whichroughly coincides with the best results of machine learning methods and hybridapproaches (which combine machine learning with elements of syntactic analysis).The authors presented their method on sentiment analysis evaluationconference’s competition.

Algorithms based on different approaches demonstratevery similar high scores of F1 (about 0.5), nevertheless, these scores are sufficientlyless than theoretical maximum that corresponds to coincidence betweenassessors. It proves that automatic sentiment detection task is still a challengingproblem need to be studied and solved..