Sentiment analysis using product review data is a machine learning project presentation for Data Mining Course.

1. Sentiment Analysis
Using Product
Review

2. Outline
 Introsuction
 Need of Sentiment Analysis
 Application
 Approch
 Implementation
 Relevent Data Set
 Advantages
 Conclusion
 Bibilography
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3. Introduction
Sentiment analysis tries to uncover emotions in the text. By analyzing movie
reviews, customer feedback, support tickets, companies may discover many
interesting things. So learning how to build sentiment analysis models is
quite a practical skill.
For the accurate classification of sentiments, many researchers have made
efforts to combine deep learning and machine learning concepts in recent
years. This section briefly describes the numerous studies related to
sentiment analysis of web contents about users' opinions, emotions, reviews
toward different matters and products using deep learning techniques.
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4. What is Sentiment Analysis
 Sentiments are feelings, opinions, emotions, like/dislikes, good/bad
 Sentiment Analysis is a Natural Languages Processing and Information
Extraction task that aims to obtain writer’s feelings expressed in
positive or negative comments, questions and requests, by analyzing a
large numbers of documents.
 Sentiment Analysis is a study of human behavior in which we extract user
opinion and emotion from plain data or text
 Sentiment Analysis is also known as Opinion Mining
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5. Example
 User’s Opinion :
 Person 1 : It’s a good product. (Positive Statement)
 Person 2 : Nah! I didn’t like it at all. (Negative Statement)
 Person 3: The new Infinity T-shirt is awesome! (Positive statement)
 Polarity :
 Positive
 Negative
 Complex
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6. Need of Sentiment Analysis
 Rapid growth of available subjective text on the internet
 Web 2.0
 Future Online Shops
 To make decision
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7. Application
 Business and Organization
 Brand Analysis
 New Product perception
 Product & Service benchmark
 Individuals
 Purchasing a product or using a service
 Finding best option in sports, movies, etc.
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8. Application
 NLP
 Use semantic to understand the language
 Use SentiwordNet
 Machine Learning
 Don’t have to understand the meaning
 User classifiers such as Naïve Byes, SVM, Linear
Regression, Count Vectorizer etc.
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9. Related Work:
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10. Implementation
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DataSet Pre-Processing
Preparing
Training Set
Preparing Test Set
Train
Classifier
Test
Classifier

11. Implementation
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Data
Tokenization
Sentence Splitter
Adjective Extraction
SentiWordNet Interpretation
Aggregating Scores

12. Our Relevent Data Set
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13. Our Relevent Data Set
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14. Our Relevent Data Set
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15. Advantages
▪ A lower cost than traditional methods of getting customer
insight.
▪ A faster way of getting insight from customer data.
▪ As 80% of all data in business consists of words, the
Sentiment Engine is an essential tool for making sense of it
all.
▪ More accurate and insightfull customer perceptions and
feedback.
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16. We have seen that Sentiment Analysis can be used for
analyzing opinions in Product Reviews where a third person
narrates his views. We also studied NLP and Machine Learning
approaches for Sentiment Analysis. Sentiment analysis has
Strong commercial interest because Companies want to know how
their products are being perceived and also Prospective
consumers want to know their existing user think.
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17.  Singla, Zeenia; Randhawa, Sukhchandan; Jain, Sushma (2017). [IEEE 2017 International Conference on
Intelligent Computing and Control (I2C2) - Coimbatore, India (2017.6.23-2017.6.24)] 2017
International Conference on Intelligent Computing and Control (I2C2) - Sentiment analysis of
customer product reviews using machine learning. , (), 1–5. doi:10.1109/I2C2.2017
 Severyn, Aliaksei; Moschitti, Alessandro (2015). [ACM Press the 38th International ACM SIGIR
Conference - Santiago, Chile (2015.08.09-2015.08.13)] Proceedings of the 38th International ACM
SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15 - Twitter
Sentiment Analysis with Deep Convolutional Neural Networks. , (), 959–962.
doi:10.1145/2766462.2767830
 Xing Fang and Justin Zhan DzSentiment analysis using product review datadz Fang and Zhan Journal of
Big Data (2015) DOI: 10.1186/s40537-015-0015-2
Bibilography
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18.  T. Chen, R. Xu, Y. He, Y. Xia, and X. Wang, Using a Sequence Model for Sentiment Analysis, no.
August, pp. 3444, 2016.
 L. Yanmei and C. Yuda, Research on Chinese Micro-Blog Sentiment Analysis Based on Deep Learning,
2015 8th Int. Symp. Comput. Intell. Des., pp. 358361, 2015.
 Pimpalkar, A., Wandhe, T., Kene, M., & Rao, M. S. (2014). Review of online product using rule
based and fuzzy logic with Smiley’s. International Journal of Computing and Technology.
Modha, J. S., Modha, S. J., & Pandi, G. S. (2013). Automatic sentiment analysis for unstructured
data.
Bibilography Cont.
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19. T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector
Space, Arxiv, no. 9, pp. 112, 2013.
Fang, Xing; Zhan, Justin (2015). Sentiment analysis using product review data. Journal of Big Data,
2(1), 5–. doi:10.1186/s40537-015-0015-2
Rajkumar S. Jagdale, Vishal S. Shirsat and Sachin N. Deshmukh Sentiment Analysis on Product Reviews
Using Machine Learning Techniques https://link.springer.com/chapter/10.1007/978-981-13-0617-4_61
 Sentiment LearninWei Wei , Jon Atle Gulla (2010)g on Product Reviews via Sentiment Ontology Tree
https://aclanthology.org/P10-1042.pdf
Bibilography Cont.
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20. Thanks!
ANY QUESTIONS?
You can find me at:
@username
user@mail.me
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