Rhetorical Structure Theory for polarity estimation: An experimental study

Abstract

Sentiment Analysis tools often rely on counts of sentiment-carrying words, ignoring structural aspects of content. Natural Language Processing has been fruitfully exploited in Text Mining, but advanced discourse processing is still non pervasive for mining opinions. Some studies, however, extracted opinions based on the discursive role of text segments. The merits of such computationally intensive analyses have thus far been assessed in very specific, small-scale scenarios. In this paper, we investigate the usefulness of Rhetorical Structure Theory in various Sentiment Analysis tasks on different types of information sources. First, we demonstrate how to perform a large-scale ranking of individual blog posts in terms of their overall polarity, by exploiting the rhetorical structure of a few key evaluative sentences. In order to further validate our findings, we additionally explore the potential of Rhetorical Structure Theory in sentence-level polarity classification of news and product reviews. Our most valuable polarity classification features turn out to capture the way in which polar terms are used, rather than the sentimentcarrying words per se.

Using rhetorical structure in sentiment analysis

“…We consider six different weighting schemes. Two serve as baselines and are applicable to the baseline sentiment scoring approach as defined in Equations (1) and (5). The others apply to our three RST-based approaches as defined in (2) and (6), in (3) and (7), and in (4) and (8), respectively.…”

“…Since accounting for such structural aspects of a text enables better understanding of the text, 15 we hypothesize that guiding orientation of text is determined by the combined semantic orientations of its constituent phrases. This compositionality can be captured by accounting for the grammatical 20 or the discursive 4,5,8,22,24 structure of text. RST 13 is a popular discourse-analysis framework, splitting text into rhetorically related segments that may in turn be split, thus yielding a hierarchical rhetorical structure.…”

“…24 Another, more elaborate, method of utilizing RST in sentiment analysis accounts for the different types of relations between nuclei and satellites, 8 yielding improvements over methods that only distinguish nuclei from satellites. 5,8 Polarity Classification Guided by Rhetorical Structure Existing methods do not use RST to its full extent yet typically focus on top-level splits of sentence-level RST trees and thus employ a rather shallow, coarse-grain analysis. However, in RST, rhetorical relations are defined within a hierarchical structure that could be accounted for.…”

Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees

“…Examples include approaches that focus on the top-split (i. e. the root node) of the discourse tree and scale the relative importance based on (hand-crafted) weights (Taboada et al, 2008;Heerschop et al, 2011;Hogenboom et al, 2015b). The underlying weights can also be optimized using logistic regression (Chenlo et al, 2014). Hierarchy labels at leaf level also facilitate a more fine-grained evaluation (Hogenboom et al, 2015b), even though the discourse tree from above is neglected.…”

Towards a top-down approach for an automatic discourse analysis for Basque: Segmentation and Central Unit detection tool

“…Discourse parsing is a very challenging task and several authors have shown that discourse structure is crucial in obtaining a better understanding of texts. Exploiting discourse structure information adequately could be the key to improving different NLP tasks such as: i ) summarization [2], ii ) complex question answering [3] iii ) opinion mining [4] and sentiment analysis [5–7].…”