The document summarizes an aspect-based sentiment analysis project that identifies aspects of entities and the sentiment expressed for each aspect in reviews. It discusses the main sub-problems of aspect extraction, category detection, polarity analysis, and category polarity. It then provides details on the algorithms and libraries used to implement solutions for each sub-problem, including using conditional random fields for aspect extraction, an SVM model for category detection, and dependency parsing with a graph approach for polarity analysis of multiple aspects.

1. Aspect Based Sentiment
Analysis

2. Course : Information Retrieval and Extraction [CSE474]
Mentor : Aditya Joshi
Professor : Dr. Vasudeva Varma
Team 2
Rishi Kathera (201201019)
Khushbu (201405514)
B H Satwik (201307682)
Deepak kathayat (201101213)

3. Details
Course: Information Retrieval and Extraction

4. Introduction
In aspect based sentiment analysis (ABSA), the aim of the
project was to identify the aspects of entities and the
sentiment expressed for each aspect. The ultimate end goal is
to be able to generate summaries listing all the aspects and
their overall polarity. The outcome will be average sentiment
for each aspect of an entity. The final scope of the project is
when given a set of all reviews of a subject, the calculation of
polarities of all the aspects of the subject can be automated.

5. What is Sentiment Analysis?
Sentiment analysis (also known as opinion
mining) refers to the use of natural language
processing, machine learning, text analysis
and computational linguistics to identify and
extract subjective information in source
materials.

6. Libraries Used in Implementation
-NLTK
-Stanford CoreNLP
-WordNet
-SentiWordNet
-SciKit Learn

7. Algorithm / Implementation

8. The main problem of ABSA was sub-divided into 4 major sub-
problems:
-Aspect Extraction: Implement a fully functioning domain
specific NER system.
-Category Detection: Implement a model to predict the
category in which the aspect being described belongs to.
-Polarity Analysis : Given an aspect and the review to which it
belong, implement a rule-based/ ML approach to finding out
the sentiment.
-Category Polarity: After finding out the polarities of individual
aspects, summarize the reviews as polarities of various
categories upon which the subject was reviewed.
The Sub-Problems

9. Aspect Extraction
-To solve this problem, a combined approach including both ML and rule based
approaches was taken.
-For the first part, an NER model was implemented using Conditional Random
Fields (CRFs) making use of different types of contextual information along with
a variety of features such as word prefixes and shapes that are helpful in
predicting the different named entity (NE) classes.
-The model uses inputs of the form - The_O service_ASPBEGIN at_O
Saul_ASPBEGIN Martin_ASPCONT is_O the_O best_O.
-These interdependencies are learnt and tagged according by the CRF model.
-In case this fails to identify aspects (generally for vague reviews), a rule-based
model which identifies all noun-phrases and then filters out the common ones
was implemented.

10. Aspect Sentiment Analysis
When multiple aspects are present in a sentence, general
purpose sentiment analysers are not quite useful as various
aspects may portray conflicting polarities.
Ex: The service was splendid but the food was inedible.
In this example, the extracted entities give out opposite
polarities and hence a decentralized approach has to be taken
for this task.
When there exists only 1 aspect in the review, a general
purpose and normally more accurate sentiment analysis
model can be used.

11. The Dependency Graph
have
may
life
phone
poor
battery
screen
beautiful
has
Aspects
Adjective closer to
“screen”, so has
more weight in
deciding sentiment
of “screen”.
Adjective closer to
“battery life”, so has
more weight in
deciding sentiment
of “battery life”.
➔Consider the sentence “The phone may have poor battery life, but has a beautiful
screen”.
➔We perform dependency parsing using Stanford CoreNLP and establish dependencies
among words in the sentences.
➔Using these dependencies a graph is constructed with directly connected dependent
words as neighbors.
➔The previously extracted aspects are taken as root nodes and BFS is performed on the
graph.
➔At each level, the sentiments of the words are found out and as we traverse deeper into
the graph, the weights of these sentiments affecting the aspects reduces exponentially.
➔Thus, the words affecting the aspect directly (directly “depend”ent) are accounted for and
the sentiment is found out for the aspect.

12. Category Detection
-Using a pre-tagged corpus, an SVM model was used to learn
the corpus to predict the category to which the extracted
corpus belonged.
-This is not exactly equal to categorizing sentences directly as
the aspect plays a vital role in deciding the output.
-In SVM, the TF values for the aspects in manually increases
to value where it affects the output as per requirements.
-The feature space considering all the n-grams and eliminating
stop words comes to around 8000 dimensions.

13. Category Polarities
When the polarities of the aspects are found, the
corresponding polarities of the categories is
tuned accordingly and the final polarity of a
category is maintained and updated with each
review.

14. Screens

16. “Thank You