Detecting speculations, contrasts and conditionals in consumer reviews

Abstract

A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an Fscore of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional.

A deep-learning approach to mining conditions

“…They devised a feature model that helps machine-learn a regressor that computes the polarity of a conditional sentence, but they did not report on a proposal to mine their conditions; they assumed that the sentences were pre-classified as either conditional or non-conditional sentences by a person. Recently, Skeppstedt et al [16] presented a complementary proposal that can automatically classify a sentence in such categories, but neither was it their goal to mine their conditions.…”

An encoder–decoder approach to mine conditions for engineering textual data

“…Unfortunately, they assumed that the sentences were previously labelled so as to make the conditions explicit and they did not provide their dataset. Recently, Skeppstedt et al (2015) presented a complementary method that can automatically classify a sentence as speculative, contrast, or conditional, but neither was their goal to mine conditions. They worked on a modified version of the SFU Review Corpus 1 that included such categories; unfortunately, their version of the dataset was not published.…”

Torii: An aspect‐based sentiment analysis system that can mine conditions

“…In the literature, Narayanan et al, Skeppstedt et al, Mausam et al, Mausam, Chikersal et al, and Nakayama and Fujii have worked on related proposals. Narayanan et al highlighted the problems of not dealing with conditions in the field of opinion mining.…”

“…Their proposal is a machine‐learning model to compute the sentiment of a conditional sentence, but they did not report on a proposal to mine the conditions; they assumed that the sentences were previously labeled so as to make the conditions explicit. Recently, Skeppstedt et al presented a complementary proposal that can automatically classify a sentence as speculative, contrast, or conditional, but neither was it their goal to mine conditions. The naivest proposals to mine conditions are based on searching for user‐defined patterns that rely on syntactic anchors.…”