Detecting Turnarounds in Sentiment Analysis: Thwarting
Ramteke, Ankit and Malu, Akshat and Bhattacharyya, Pushpak and Nath, J. Saketha

Article Structure

Abstract

Thwarting and sarcasm are two uncharted territories in sentiment analysis, the former because of the lack of training corpora and the latter because of the enormous amount of world knowledge it demands.

Credits

The authors thank the lexicographers at Center for Indian Language Technology (CFILT) at IIT Bombay for their support for this work.

Introduction

Although much research has been done in the field of sentiment analysis (Liu et al., 2012), thwarting and sarcasm are not addressed, to the best of our knowledge.

Definition

Thwarting is defined by Pang et al., (2008) as follows:

Building domain ontology

Domain ontology comprises of features and entities from the domain and the relationships between them.

A rule based approach to thwarting recognition

As per the definition of thwarting, most of the thwarted document carries a single sentiment; however, a small but critical portion of the text, carrying the contrary sentiment, actually decides the overall polarity.

A Machine Learning based approach

Manual fixing of relative weightages for the features of the product is possible, but that would be ad hoc.

Results

Experiments were performed on a dataset obtained by crawling product reviews from Ama-zonl.

Conclusions and Future Work

We have described a system for detecting thwarting, based on polarity reversal between opinion on most parts of the product and opinion on the overall product or a critical part of the product.

Topics

machine learning

Appears in 6 sentences as: machine learning (6)
In Detecting Turnarounds in Sentiment Analysis: Thwarting
  1. In this paper, we propose a working definition of thwarting amenable to machine learning and create a system that detects if the document is thwarted or not.
    Page 1, “Abstract”
  2. We show that machine learning with annotated corpora (thwarted/non-thwarted) is more effective than the rule based system.
    Page 1, “Abstract”
  3. In section 5 we discuss a machine learning based approach which could be used to identify whether a document is thwarted or not.
    Page 2, “Introduction”
  4. We now propose a machine learning based approach to detect thwarting in documents.
    Page 4, “A Machine Learning based approach”
  5. Table 1 shows the results for the experiments with the machine learning model.
    Page 5, “Results”
  6. Table 1: Results of the machine learning based approach to thwarting detection
    Page 5, “Results”

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sentiment analysis

Appears in 6 sentences as: sentiment analysis (6)
In Detecting Turnarounds in Sentiment Analysis: Thwarting
  1. Thwarting and sarcasm are two uncharted territories in sentiment analysis , the former because of the lack of training corpora and the latter because of the enormous amount of world knowledge it demands.
    Page 1, “Abstract”
  2. Although much research has been done in the field of sentiment analysis (Liu et al., 2012), thwarting and sarcasm are not addressed, to the best of our knowledge.
    Page 1, “Introduction”
  3. Thwarting has been identified as a common phenomenon in sentiment analysis (Pang et al., 2002, Ohana et al., 2009, Brooke, 2009) in various forms of texts but no previous work has proposed a solution to the problem of identifying thwarting.
    Page 1, “Introduction”
  4. This definition emphasizes thwarting as piggy-backing on sentiment analysis to improve the latter’s performance.
    Page 2, “Definition”
  5. The basic objective for creating a thwarting detection system was to include such a module in the general sentiment analysis framework.
    Page 5, “Results”
  6. Thus, using document polarity as a feature contradicts the objective of sentiment analysis , which is to find the document polarity.
    Page 5, “Results”

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human annotator

Appears in 3 sentences as: human annotator (3)
In Detecting Turnarounds in Sentiment Analysis: Thwarting
  1. Some additional features get added by human annotator to increase the coverage of the ontology.
    Page 2, “Building domain ontology”
  2. The abstract concept of storage is contributed by the human annotator through his/her world knowledge.
    Page 3, “Building domain ontology”
  3. Step 2: The features thus obtained are arranged in the form of a hierarchy by a human annotator .
    Page 3, “Building domain ontology”

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SVM

Appears in 3 sentences as: SVM (2) svm (1)
In Detecting Turnarounds in Sentiment Analysis: Thwarting
  1. We use the SVM classifier with features generated using the following steps.
    Page 4, “A Machine Learning based approach”
  2. We used the CVX3 library in Matlab to solve the optimization problem for learning weights and the LIBSVM4 library to implement the svm classifier.
    Page 5, “Results”
  3. This ontology guides a rule based approach to thwarting detection, and also provides features for an SVM based learning system.
    Page 5, “Conclusions and Future Work”

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