Transcript: #StandardsGoals for 2024: What’s new for BISAC - Tech Forum 2024
Aspect Based Sentiment analysis of Afaan Oromoo Movie reviews using machine learning techniques
1. BULE HORA UNIVERSITY
COLLEGE OF INFORMATICS
COMPUTER SCIENCE DEPARTMENT
Aspect Based Sentiment Analysis
of Afaan Oromoo Movie Reviews Using
Machine Learning Techniques
By
Obsa Gelchu
Advisor: Kula kekeba (PhD)
2. Presentation Outline
Introduction
Statements of Problem
Research Questions
Objective of the study
Scope and Limitation
Significance of the Study
Summary of Related work
Research Gap
Research Methodology
Experimental Results and Discussion
Conclusion, Contribution and Recommendation
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3. Introduction
Sentiment Analysis (SA)
SA is increasingly viewed as vital task both from
academic and commercial stand point.
Basically, SA is NLP techniques used to identify
and categorize attitude of reviewers to respective
topics as positive, negative, or neutral.
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5. Introduction…
Document level and Sentence level SA
Considered overall polarity of the reviewed text
regardless attributes of an entity.
However, Comments of the reviewer may contain
different aspects/features.
Ex: “The story of the movie is good but the acting
of the actors is awful”.
Aspect terms: {message, acting}
Aspect Polarity: {message, #positive, acting,
#negative}
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6. Introduction…
Aspect Based Sentiment Analysis (ABSA)
ABSA is sub-level of SA which extracts aspect of
each topic from reviewer sentence with its
corresponding sentiment polarity.
Ex: “The actor is so good, but this movie just horrible”.
Aspect terms: {actor, movie }
Aspect polarity: {actor, #positive , movie, #negative}
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7. Statement of the Problem
With the emergency of social media, comments to
particular entity in business industry are increasing
daily from the behalf of users/customers.
Opinions can influence the thinking of human
beings as humans always want to know the
opinions of others in every decision they may
made.
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8. Statement of the Problem…
To summarize thousands opinions of users to
respective topics, different scholars were
investigated SA only at document and sentence
level regardless of its features.
Comments of the users may contain different
aspects/features.
Ex: “The actor is so good, but this movie just
horrible”.
Aspect terms: {actor, movie}
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9. Statement of the Problem …
Analyzing opinion of reviewers only at document
and sentence level SA cannot provide sufficient
information.
To analyse the opinions of customers at fine-
grained level, ABSA was proposed to analyse
Afaan Oromoo movie reviews using machine
learning techniques.
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10. Research Questions
1. Which Machine learning model is most
appropriate for Afaan Oromoo ABSA for movie
reviews?
2. What are the best attributes for Afaan Oromoo
movie reviews for ABSA?
3. What are the main challenges in building Afaan
Oromoo ABSA dataset for movie reviews?
4. What is the performance of the proposed ABSA
system?
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11. Objectives
General objective
To design and develop ABSA model for Afaan
Oromoo movie reviews using machine learning
techniques.
Specific Objectives
To review SA related work for ABSA of AO movie
reviews.
To investigate appropriate strategies to prepare
ABSA dataset for Afaan Oromoo movie reviews.
To explore how aspect-based opinions could be
designed and implemented for Afaan Oromoo
movie entertainment.
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12. Objectives…
Specific Objectives….
To develop different machine learning models for
Afaan Oromoo ABSA for movie reviews.
To evaluate the performance of the proposed
models for Afaan Oromoo movie reviews.
To prepare Afaan Oromoo ABSA dataset for
movie reviews.
Based on the gained result, to propose a set of
improvements and to suggest recommendations.
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13. Scope and Limitation of Study
The objective of the study is to design and
develop ABSA model for Afaan Oromoo movie
reviews using machine learning techniques.
For this purpose we have focused on:
Afaan Oromoo movie comments and its
feedback.
Aspects of AO movies like:
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diraamaa
sagalee
itti-fufinsa
yeroo
taatoo
ergaa
uffannaa
14. Scope and Limitation of Study…
Aspect sentiment prediction:
positive and negative
Supervised Machine learning techniques:
Random Forest,
Logistic Regression,
Support Vector Machine and
Multinomial Naïve Bayes
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15. Scope and Limitation of Study…
In this ABSA, the following points have been
excluded:
Opinion holder
Time at which comment was posted
Likes, feedback opinion holder
Comparative opinion
Images, Emojis, audio and video
Texts other than Afaan Oromoo
Neutral sentiment polarity
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16. Significance of Study
Business intelligence
Brand/ service insight
Competitive Analysis
Opinion Mining
Voice of Employee
Voice of Customer
Reputation management
Social media listening
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17. Summary of Related work
Both national and international work on SA have
reviewed– ABSA with different ML techniques.
Summary of Amharic Related work
Summary of Afaan Oromoo Related work
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18. Research / Knowledge Gap
To the best of our knowledge, in previous works
examined there was no work has been
done on Afaan Oromoo ABSA using machine
learning techniques.
The proposed work on the ABSA for Afaan
Oromoo movie reviews using machine learning
techniques is the first work and Original.
As a result, this research was attempted to cover
this gap in this investigation.
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20. Research Methodology…
Data Preprocessing
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Collecting AO
movie reviews
Removing
URL, Emojis
Removing
Punctuation
Normalization
Convert to
lowercase
AO stopword
Removal
Tokenization Stemming
Aspect term
extraction and
polarity prediction
31. Conclusion
SA is the field NLP which analysis the opinions
of reviewers to respective aspects of entity as
positive, negative or neutral.
SA has been studied in three levels:
Document level
Sentence level and
Aspect/feature based SA.
In this thesis, ABSA of Afaan Oromoo movie
reviews was investigated.
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32. Conclusion
In this thesis ABSA was investigated for Afaan
Oromoo movie reviews using:
Random Forest,
Logistic regression,
SVM and MNB.
BoW and TFIDF was used in combination with
selected algorithms.
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33. Conclusion
The generated reports shows that almost all the
same evaluation parameters were generated by
using all proposed algorithms in combination with
both BOW and TFIDF.
The experimental results revealed that, the
classification report generated by all selected
algorithms have been found to be promising for
ABSA of AO movie reviews.
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34. Contribution
We have proposed the ABSA model for the Afaan Oromoo movie
reviews with good performance evaluation metrics.
We have built ABSA dataset by collecting 2800 Afaan Oromoo movie
reviews.
We came up with the guidelines to label the Afaan Oromoo ABSA
datasets for movie reviews.
The thesis can be used as a baseline for Aspect Based Sentiment-mining
related research works for opinionated Afaan Oromoo text.
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35. Recommendation
Points to be considered in future works are:
Emojis
Multi-class classification
Data from other social media
Standard dataset
Developing prototype
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