Aspect Based Sentiment Analysis

Introduction

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen ; food, service). By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.

Problem Statement

This task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.

Challenges Faced

Aspect term extraction,
We used rule based method, and some of the aspect terms weren’t extracted correctly by the rules.
We assumed all sentences are grammatically correct which is not always the real life scenario.
Aspect term polarity,
In sentences with many aspect terms, it was difficult to assign polarity.

Dataset

We used the SemEval 2016 dataset available at: http://metashare.ilsp.gr:8080/repository/browse/semeval-2014-absa-restaurant-reviews-trial-data/1790ab94464211e388f5842b2b6a04d79bb0323cac4f49939bf5b99878dc38be/ This dataset consists of restaurant reviews presented in an XML file. Contains around 100 reviews, with varied number of aspect terms in each.

Tools Used

Stanford CoreNLP Parser
NLTK

Phases of the Project

Phase 1

Task 1

Aspect term Extraction This task deals with extracting the aspect terms from the reviews. Given a set of sentences with pre-identified entities (e.g., restaurants), identify the aspect terms present in the sentence and return a list containing all the distinct aspect terms. An aspect term names a particular aspect of the target entity.

Task2

Aspect Polarity Detection For a given set of aspect terms within a sentence, determine whether the polarity of each aspect term is positive, negative, neutral or conflict (i.e., both positive and negative).

Phase 2

Task 2

Given a predefined set of aspect categories (e.g., price, food), identify the aspect categories discussed in a given sentence. Aspect categories are typically coarser than the aspect terms of Subtask 1, and they do not necessarily occur as terms in the given sentence.

Task 4

Given a set of pre-identified aspect categories (e.g., {food, price}), determine the polarity (positive, negative, neutral or conflict) of each aspect category.

Results

The system was run on SemEval 2016 dataset for restaurant reviews. The evaluation outputs are obtained as follows.

For Aspect Terms

Aspects

System Aspect Terms=747
Gold Aspect Terms=737
Pre: 0.2570281 (192/747)
Rec: 0.2605156 (192/737)
F:0.2587601

For polarity