Aspect Level Sentiment Classification For Arabic Language Mahmoud El Razzaz ISSR.CU Under the Supervision of Dr. Mohamed Farouk Prof. Dr. Hesham A. Hefny 1

Agenda 1.Introduction 2.Problem definition 3.Difficulties and chalenges 4.Related work 5.Objective 6.Work plan 7.References

Introduction to Sentiment Analysis Introduction to Sentiment Analysis 3

Sentiment Classification is a sub domain of text Classification or text categorization. Text classification is concerned with automatically identify the category or the domain of a text document (Political, Financial, … etc.,) What is Sentiment Analysis 4

[ Sentimental ] My Phone is horrible! [ Factual ] My phone has 5MP camera [ Sentimental ] Identifying the opinion in a piece of text It can be generalized over a wider set of emotions My Phone is awesome! What is Sentiment Analysis 5

Advantages >>A lower cost than traditional methods of getting customer insight. >>A faster way of getting insight from customer data. >>The ability to act on customer suggestions. >>Identifies an organisation's Strengths, Weaknesses, Opportunities & Threats (SWOT Analysis). >>More accurate and insightful customer perceptions and feedback. 6

Sentiment Analysis at different levels 7

The task at this level is to classify whether a whole opinion document express a positive or negative sentiment. Researchers developed machine learning classifiers to classify document level sentiments for both English Language [1] and Also Arabic Language [2] Document Level Sentiment Analysis References: [1] Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of Conference on Empirical methods in Natural Language processing (EMNLP-2002) [2] Mohamed Aly and Amir Atiya: LABR: A Large Scale Arabic Book Reviews Dataset. In Proceedinds of the 51st Annual Meeting of the Association for Computational Linguistics, Pages Sofia, Bulgaria, August

This level of Analysis assumes that each document expresses opinions on a single entity (e.g., a single product). Thus, it is not applicable to documents which evaluate or compare multiple entities. Document Level Sentiment Analysis References: [1] Example in English: positive Sentiment about a smart phone [1] “My mpop is very amazing even thought its battery drains fast the performance and the speed of the phone is very good even in playing high graphic games the camera is bright ” Example In Arabic: positive Sentiment about a book [2] “ الكتاااااااااب جامد جداااا. وعجبنى اسلوب الكوميدية بتاعتة رغم ان ليا بعض الانتقادااااات فية بس بوجه عام حلووو وعميق ” 9

Sentence Level Sentiment Analysis The task at this level goes to the sentences and determines whether each sentence expressed a positive, negative, or neutral opinion. Neutral usually means no opinion. The poverty of India is decreasing Ex., 10 Reference: N. Farra, E. Challita, R. Assi, and H. Hajj. Sentence-Level and Document-Level Sentiment mining for Arabic Texts. In proceedings of International Conference on data mining workshops. Pages IEEE, 2010

Aspect Level Sentiment Analysis Both the document level and the sentence level analyses do not discover what exactly people liked and did not like. Aspect Level Sentiment Analysis is based on the idea that an opinion consists of a sentiment (positive or negative) and target of opinion. Realizing the importance of opinion targets also helps us understand the sentiment analysis problem better. For example, “although the service is not that great, I Still love this restaurant.” clearly has a positive tone, we can not say that this sentence is entirely positive. In fact it is positive about the restaurant but negative about the service. 11

Aspect Level Sentiment Analysis Example “My mpop is very amazing even thought its battery drains fast the performance and the speed of the phone is very good even in playing high graphic games the camera is bright ” The Sentiment on mpop, performance, speed and camera is positive. The sentiment on the battery is negative. The mpop, performance, speed and battery are the opinion targets 12

Advantages of Aspect Level Sentiment Analysis Based on this level of analysis a structured summary of opinions about entities and their aspects can be produced. Reference: Tun Thura Thet, Jin-Cheon Na and Christopher S.G. Khoo: “Aspect-based sentiment analysis of movie reviews on discussion boards” Journal of Information Science

Advantages of Aspect Level Sentiment Analysis Thus it would be more useful for both customers and service provider or product producers. - For product producers or service providers they would know exactly what are the main aspects of the product/service that customers are not satisfied about rather than just knowing that customers are not satisfied about the service or product in general. 14

Advantages of Aspect Level Sentiment Analysis For customers it would be more important and this is because each customer usually concerned about a few number of product features “Aspects” and do not care about the other features. Thus customers may concentrate on the aspects the care much about rather than having an overall review of other users about the product or service. For example some may be concerned about the life time of the battery, the quality of the camera and the clearance of the screen while shows no concern about the color, weight and the insurance period of the mobile phone thus using aspect analysis would give customers a brief summary of user opinions specifically about each aspect of the mobile so he can decide which is better for him. 15

Challenges and Difficulties Both the Document Level and sentence level classifications are already highly Challenging. The aspect-level is even more difficult. It constricts or several sub-problems: 1- Entity Extraction. 2- Entity categorization (picture, image and photo are the same aspects for cameras) Each entity category should have a unique name in a particular application. 3- implicit Entities (this book is expensive) 16

17 Difficulties related to Arabic language 1- Rare resources (few number of Arabic datasets are available) 2- Rare resources (few NLP tools are available for Arabic Slang) 3- The variance of Arabic dialects or tones from country to country. (ex., 3eda gamda gedan bas el battery taba3ha yefda bsor3a) 4- Some Arabic natives writes reviews in Franco Arab and some other write reviews in multiple languages. Ex., : (نوكيا Asha هاتف ممتاز لكن البطارية بتخلص بسرعة وما فيه apps كتير) Challenges and Difficulties ( continuous ) Reference: Soha Ahmed, Michel Pasquier, and Ghassan Qadah: “Key issues in conducting sentiment analysis on arabic social media texts” 2012

18 Related work Recently researchers bayed more attention to the problem of sentiment analysis for Arabic language such as: - Mohamed El Arnaoty et al., who provided “a machine learning approach for opinion holder extraction in Arabic language” Mohamed Aly et al., who provided “A Large Scale Arabic Book reviews Data Set” Also a Survey on Sentiment And Subjectivity Analysis of Arabic were introduced by Mohamed Korayem et al., in “Subjectivity and Sentiment Analysis of Arabic: A Survey” 2012.

19 - Furthermore the difficulties of applying sentiment classification in Arabic Language were disused by Soha Ahmed et al., in “Key Issues in Conducting Sentiment Analysis on Arabic Social Media Text” Related work

Some of the Review Websites (book reviews) (mobile phones reviews) (digital cameras reviews) (restaurants reviews) ( reviews on multiple subjects ) (movies reviews) 20

Example of a Review website 21

22 Objective Construct An aspect level sentiment classification system to automatically Summarize the Arabic sentiments of users of a specific product or service.

23 Work plan 1. Overview of Data collection 2. Overview of data preprocessing (entity extraction, entity categorization, feature selection, and feature extraction) 3. Overview of the Sentiment Analysis levels and techniques 4. The proposed approach for Sentiment Analysis: Aspect Level Sentiment classification. 5. Testing the proposal approach and comparing the results with related work. 6. Conclusion and future work.

24 Thank you