This document discusses sentiment analysis of Nepali text using Naive Bayes. It presents the problem of sentiment analysis being in a research phase for Nepali language. The objectives are to analyze customer opinions on products/services and study political views. The methodology involves data collection, preprocessing like tokenization and removal of stop words, vectorization using TF-IDF, training and testing a Naive Bayes model, and measuring accuracy. Limitations include low accuracy for reviews with English and Nepali mixed, and difficulty addressing ambiguity.

1. Sentiment Analysis
Of Nepali Text
Using Naïve Bayes
MAHESH ACHARYA
ABHISHEK SAPKOTA
ASHOK CHHETRI
RABIN BHANDARI
UNDER SUPERVISION OF
MR. BIKASH BALAMI

2. INTRODUCTION
 Contextual mining of text in Negative, Positive or Neutral
 Analysis the opinion of customer on products and services
 Study the political views of people of current affairs
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3. PROBLEM DEFINITION
 Sentiment analysis is in research phase in Nepali Language
 What if business owners make strategic plan based on customers
reviews?
 E-commerce sites not utilizing the reviews of the customers
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4. OBJECTIVES
 To analysis the opinion of customer on products and services
 To study the political views of people of current affairs
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5. SCOPE
SCOPE
 Holds benefit for ecommerce sites
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6. USE CASE DIAGRAM
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7. METHODOLOGY
Data Collection
Nepali contextual data were collected manually
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8. METHODOLOGY
Data Collection
Nepali contextual data were collected manually
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9. DATA PREPROCESSING
आह! कती राम्रो समान रहेछ यो ।
:)
अहँ! यो भनाइ ठीक छैन । :(
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आह कती राम्रो समान रहेछ यो
अहँ यो भनाइ ठीक छैन

10. अहँ यो भनाइ ठीक छैन
आह कती राम्रो समान रहेछ यो
अहँ यो भनाइ ठीक छैन
TOKENIZATION
आह कती राम्रो समान रहेछ यो

11. REMOVE STOP WORDS
आह कती राम्रो समान रहेछ यो
आह कती राम्रो समान रहेछ
अहँ भनाइ ठीक छैन
अहँ यो भनाइ ठीक छैन

12. VECTORIZE - TF(TERM FREQUENCY
)
आह कती राम्रो समान रहेछ यो
0.20 0.20 0.20 0.20 0.20
0.25 0.25 0.25 0.25
अहँ यो भनाइ ठीक छैन

13. IDF(INVERSE DOCUMENT FREQUENCY)
0.30 0.30 0.30 0.30 0.30
0.30 0.30 0.30 0.30
अहँ यो भनाइ ठीक छैन
आह कती राम्रो समान रहेछ यो

14. TFIDF(TERM FREQUENCY INVERSE
DOCUMENT FREQUENCY)
0.60 0.60 0.60 0.60 0.60
0.075 0.075 0.075 0.075
अहँ यो भनाइ ठीक छैन
आह कती राम्रो समान रहेछ यो

15. TRAIN PHASE
Naïve Bayes
Train Model
{ आह:{pos:2,neg:1,neu:1} ,कती :{pos:2,neg:1,neu:1} राम्रो :{pos:2,neg:1,neu:1}
समान :{pos:2,neg:1,neu:1} रहेछ :{pos:2,neg:1,neu:1} यो :{pos:2,neg:1,neu:1}
0.075 0.075 0.075 0.075 0.075 1(pos)
0.60 0.60 0.60 0.60 0(neg)

16. TEST PHASE
Naïve Bayes
Testing Model
0.0775 0.0775 0.0775 0.0775 0.0775
0.680 0.680 0.880 0.180

17. PREDICTION
User Input
यो ठाउँ घुम्न को लागि राम्रो छ । 
Remove Punctuation
यो ठाउँ घुम्न को लागि राम्रो छ
(Note : User input also vectorize like above method.)

18. RESULT
Positive

19. ACCURACY MEASURE
SN. Train Data (%) Precision (%) Recall (%) F-Score (%)
1. 60 56.27 53.68 54.68
2. 70 57.55 54.91 56.20
3. 80 59.03 57.05 58.02
4. 90 61.45 59.33 60.37

20. LIMITATION
 The accuracy of the reviews is low when there is the mixture of
Nepali as well as English language in comments.
 Ambiguity can occurs and semantic analysis is difficult to address.
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21. TOOLS USED
Front-End Back-End

22. Demo

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