uloaded to avoid duplication bu other researcher. 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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4. Introduction…
Levels of Sentiment Analysis
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Sentiment
Analysis
Document
level
Sentence
level
Aspect
level

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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19. Research Methodology
General Methodology followed
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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

21. Research Methodology…
Dataset Annotation
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Dataset
Annotation
Aspect Term
Extraction
Lexicon
based
Topic
modelling
Hand-crafted
lexicon
Aspect Polarity
prediction

22. Research Methodology…
Dataset Annotation…
Task 1:Aspect term extraction
Ex, “diraamaa baay’ee bareedaa dha; Garuu,
uffaannaan isaanii gaarii miti”.
Aspect Terms: {diraamaa, uffannaa}
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23. Research Methodology…
Dataset Annotation…
Task 1:Aspect term extraction
Ex2: “diraamaan baay’ee bareedaa dha; garuu maal
godhan baay’ee gabaabbate”.
Aspects Terns: {diraamaa, yeroo}
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24. Research Methodology…
Dataset Annotation…
Task 2:Aspect sentiment prediction
Ex1: “diraamaa baay’ee bareedaa dha; Garuu,
uffaannaan isaanii gaarii miti”.
Aspect polarity: {diraamaa, #positive, uffannaa,
#negative}
Ex2: “diraamaan baay’ee bareedaa dha; garuu
maal godhan baay’ee gabaabbate”.
Aspect Polarity: {diraamaa, #positive, yeroo,
#negative}
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25. Research Methodology…
Feature Vectorization
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Feature
Vectorizer
TFIDF
TfidfVectorizer
BOW
CountVectorizer

26. Research Methodology…
Implemented
Algorithms
Random Forest
Logistic
Regression
SVM and
MNB
Model Evaluation
Parameters
Accuracy,
 precision,
 recall
F1-score
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27. Research Methodology…
Proposed architecture
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28. Result and Discussion
Extracted Aspect term per reviews
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29. Result and Discussion…
Aspect term polarity prediction
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30. Result and Discussion…
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Models with feature selection
methods
Accuracy precision recall F1-score
Random Forest + BOW 88% 93% 90% 91%
Random Forest + TFIDF 88% 93% 89% 91%
Logistic Regression + BOW 87% 95% 87% 91%
Logistic Regression + TFIDF 87% 96% 87% 91%
SVM + BOW 87% 95% 86% 91%
SVM + TFIDF 88% 93% 90% 91%
MNB + BOW 88% 93% 90% 91%
MNB+TFIDF 88% 94% 88% 91%

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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36. Thank You for your
attention!!!
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