Potentials and Limitations Of Automated Sentiment Analysis of Weblogs

GEORG-AUGUST-UNIVERSITÄT GÖTTINGEN

06 - Jun -14 1

Seminar Presented by :

Gollapinni Karthik

Student Id: 11337533

Course: Masters of Applied Computer Science

Foundations

Approaches of ASA

Potentials of ASA

Limitations of ASA

Conclusions

AGENDA

Introduction

06 - Jun -14 2

INTRODUCTION(1/2) Web is an excellent source for gathering customers opinions. What others think has always been an important piece of information. 85% of the people gather information online before buying a product

1) Pre-Web Friends Customer reports

2) Post-Web Blogs Review Sites Discussion forums

06 - Jun -14 3

INTRODUCTION(2/2)

06 - Jun -14 4

150 Million Number of blogs online

1 billion Number of blog/forums readers

55 million Number of tweets in a month

180 million Number of unique visitors to twitter everyday

Answers Research Questions :-

Which application fields is ASA useful?

How far are limitations restricting the exploitation of potentials?

Introduction

Approaches of ASA

Potentials of ASA

Limitations of ASA

Conclusions

AGENDA

Foundations

06 - Jun -14 5

FOUNDATIONSWhat does Automated Sentiment Analysis mean?

´Opinion Mining´

`A system which automatically identifies the sentiments and extract the information from the emotions or options and process a finite decision of the avaliable sentiments from the given pool of content (Butler, Eugene. "Automated Sentiment Analysis”)´

Natural Processing Language(NLP)

Reshape the businesses

06 - Jun -14 6

PROCESS OF ASA

06 - Jun -14 7

What are Weblogs?‘A website which consists of discrete number of series of entries which are arranged in reverse chronological order and are often updated frequently with new information about the particular topics (Rouse, Margaret. “Weblogs”)’

Log of our times

´User-generated data´

Personal, Business or Community are 3 types of weblogs.

06 - Jun -14 8

Introduction

Foundations

Potentials of ASA

Limitations of ASA

Conclusions

AGENDA

Approaches of ASA

06 - Jun -14 9

Computer Coding Vs Human Coding

06 - Jun -14 10

DIFFERENCES BETWEEN COMPUTER CODING AND HUMAN CODING

06 - Jun -14 11

COMPUTER CODING HUMAN CODING

Uses Dictionary based or Machine Learning Methods.

Follows standard code book and coding form.

Involves software for automation process.

Involves people as coders.

Automated tabulation of variables. Human observation is recorded on pre-established variables.

06 - Jun -14 12

BUILDING BLOCKS OF ASA

5 main factors ASA looks at:

Topics: Main areas of discussion.

Aspects (subtopics and attributes): What about the topics being talked ?

Sentiment: What is the sentiment of the content?

Holder: Whose is being analyzed?

Time: When was the content posted?

LEVELSOf

ASA

Statement Level

Document Level

Aspect Level

06 - Jun -14 13

MACHINE LEARNING METHODS

06 - Jun -14 14

DICTIONARY METHOD

Introduction

Foundations

Approaches of ASA

Potentials of ASA

Limitations of ASA

Conclusions

AGENDA

Potentials of ASA

06 - Jun -14 15

METHODOLOGY FOLLOWEDFollowed ‘Literature Review’ to categorize the Areas of the Potentials and Limitations.

Used scholar.google.com to take the books and papers.

Most used books and papers:1. (Bing Liu 2011)2. (Brain Conlin 2012) 3. (Michael Gamon)4. (Kimberly Neuendarf 2002)5. (Yayan Meng 2012)6. (Adam Westerki 2013)7. (Bing Liu 2012)8. (Ben Donkor 2013)9. (Sitaram/Bernardo 2010)

06 - Jun -14 16

POTENTIAL AREAS OF ASA

06 - Jun -14 17

AreasOf

ASA

Financial Domain

IndividualNeeds

Supervised Spam

DetectionMarketing

Domain

Advertisement Placement

Product benchmarking and Market Intelligence

Opinion Summarization

Opinion search and Retrieval

Practical examples :

Analyze the comments made on the weblogs to provide better customer support.

Monitoring the Company brand. 06 - Jun -14 18

MARKETING DOMAIN

Stock Markets

Analyze market sentiments

Public Relations

Investor Relations

06 - Jun -14 19

FINANCIAL DOMAIN

INDIVIDUAL NEEDSASA system could be potentially used by :

Internet Users

Summarized view of posts for community review sites

Other decision making tasks

Examples:

1. Movie reviews or product reviews on Amazon book store (Hu and Liu, 2004)

2. Product reviews on Other websites(Hariharan et al., 2010)

06 - Jun -14 20

Posts are being abused.

Fake comments and reviews

Generic reviews

Random texts

Example : - “Get one for FREE!!! Have a look at this video first: http://www.youtube.com/watch?v=DFKYVE__Mug Just take 2 minutes and read this. This is very EASY to do…”

06 - Jun -14 21

SUPERVISED SPAM DETECTION

Introduction

Foundations

Approaches of ASA

Potentials of ASA

Conclusions

AGENDA

Limitations of ASA

06 - Jun -14 22

06 - Jun -14 23

LIMITATIONS OF ASA(1/2)Language Specific Limitations

Technological Limitations Data Specific Limitations

Words have different meanings Extraction of Entities Noisy data

Language is a barrier Cannot identify the root cause of the review

Videos and Images

Comparative and Complex statements

Entity level vs Article level Sentiment.

Large data sets and lots of spam

Scarsam statements. Ex:Irony Human Accuracy is missing. Not available for all domains

Short forms and many representations of the words.

Only three categories to categorize the reviews.

Fails to classify with respect to others prespective

Co–relationship between the sentences.

Focused on explicit opinion. Knowledge of the data

LIMITATIONS OF ASA(2/2)

06 - Jun -14 24

Limitations of Document –Level – ASA

Limitations of Statement – Level – ASA

Limitations of Aspect – Level – ASA

Need more addational details of the aspects liked and disliked.

Cannot deal with the conditional, sarcasm and question statments.

Fails in aspect extraction and aspect sentiment classification and the sentiment words can handle up to 60% of problems.

Not applicapable for weblogs and non-review forums like discussions, blogs

One common language. Not available for all the domains.

Does not provide fine grained analysis

Complex statements have different sentiments on different targets.

Noisy data.

FACTORS ASA SHOULD FOCUS

Demystifying accuracy

Content Type Filter

Entity level vs. Article level Sentiment

Human Accuracy

Sentiment Override

06 - Jun -14 25

Introduction

Foundations

Approaches of ASA

Potentials of ASA

Limitations of ASA

AGENDA

Conclusions

06 - Jun -14 26

CONCLUSIONSSentiment analysis tackleschallenging tasks that involve NLP and text mining.

There are many challenging problems yet to be solved.

Has a strong commercial interest.

Future of Sentiment AnalysisThe gap between the social media, blogs and the sentiment analysis can be covered by bridging the gap

between the insight and action.

The future can hold by using concepts of influencer analytics with analysis can help for better results.

In near future on the whole ASA can be an advantage to various social analyses, predictions and finally a master piece insight to the company social performance.

06 - Jun -14 27

Thanks for listeningThe END

06 - Jun -14 28

06 - Jun -14 29

RERERENCES Westerski, Adam. Sentiment Analysis: Introduction and the State of the Art Overview. Tech. N.p.: n.p., n.d.

Print. Ganesan, Kavita, and Hyun Duk Kim. "Opinion Mining Tutotrial." Slideshare.net. Web. Sentiment Analysis. Digital image. Itresearches.ru. N.p., n.d. Web. Liu, Bing. "Chapter 1 Sentiment Analysis: A Fascinating Problem." Sentiment Analysis and Opinion Mining. 2nd

ed. Vol. 1. San Rafael, CA: Morgan & Claypool, 2012. Pp. 7-48. Print. Butler, Eugene. "Automated Sentiment Analysis." , . 4 Dec. 2010. Lecture. McGlohon, Mary, Natalie Glance, and Zach Reiter. Star Quality: Aggregating Reviews to Rank Products and

Merchants. Tech. N.p.: n.p., n.d. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media

Local, Reach . "150 Smart Stats about Online Marketing." , . 29 Aug. 2012. Lecture. Outline of Sentiment Analysis. Digital image. Softwareunwound. N.p., n.d. Web. "How to Learn About Micro Blogging." WikiHow. Ed. Teresa. N.p., n.d. Web. "Types of Weblogs." Southbourne. N.p., n.d. Web.

REFERENCES

Neuendorf, Kimberly A. “Chapter 3 Beyond Description : An Integrative Model of Content Analysis” The Content Analysis Guidebook. 1st Edition. 1. United States Of America: Sage Publications, 2002. Pp. 35-36

Federico P. "LAPS: Sentiment Analysis Project Presentation." Nextbit. N.p., n.d. Web.

‘Sentiment Analysis -The Next Big Thing In Social Media Marketing’ "Introduction to Sentiment Analysis Applied to the Stock Market." QuantShare Trading Software. N.p., n.d.

Web. Wang, Jun. Sentiment Analysis in Practice. Publication no. 1. N.p.: n.p., n.d. Print.

Ogneva, Maria. "How Companies Can Use Sentiment Analysis to Improve Their Business“

Kaefer, Mark. Let's Get Social: Web User Preferences, Habits and Actions in Spring 2012. Rep. N.p.: Burstmedia, n.d. Print.

Hu,M., B., August 2004. Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04). Seattle, Washington, USA, pp. 168-177.

06 - Jun -14 30

REFERENCES

Hariharan, S., Srimathi, R., Sivasubramanian, M., Pavithra, S., 2010. Opining Mining and summarization of reviews in webforums. In: Proceedings of the Third Annual ACM Bangalore Conference.

(Bing Liu 2011) : Bing Liu. Sentiment Analysis and Opinion Mining, Morgan &Bollen, Johan, Huina Mao, and Xiao- Jun Zeng. Twitter Mood Predicts the Stock Market. Tech. N.p.: n.p., 2011. Print.

(Brain Conlin 2012) : Bing Liu. Sentiment Analysis and Opinion Mining, Morgan &Bollen, Johan, Huina Mao, and Xiao- Jun Zeng. Twitter Mood Predicts the Stock Market. Tech. N.p.: n.p., 2011. Print.

(Michael Gamon) : Gamon, Michael. "Linguistic Correlates of Style: Authorship Classification with Deep Linguistic Analysis Features - Microsoft Research."Http://research.microsoft.com/. International Conference on Computational Linguistics/ Coling.

(Kimberly Neuendarf 2002) : Neuendorf, Kimberly A. The Content Analysis Guidebook. 1st Edition. 1. United States Of America: Sage Publications.

(Bing Liu 2012) : Liu, Bing. Sentiment Analysis and Opinion Mining. 2nd ed. Vol. 1. San Rafael, CA: Morgan & Claypool, 2012. Print.

(Ben Donkor 2013) : Donkor, Ben. "On Social Sentiment and Sentiment Analysis." Web log post .Brnrd.me. N.p.,

06 - Jun -14 31

REFERENCES (Yayan Meng 2012) : The Truth About Sentiment & Natural Language Processing." Current

Technological Meng, Yanyan. SENTIMENT ANALYSIS: A STUDY ON PRODUCT FEATURES. Thesis. University of Nebraska, 2012. Lincoln: DigitalCommons, 2012. Print. Miller, George A., Richard Beckwith, Christiane Fellbaum, Derek Gross, and Katherine.

(Adam Westerki 2013) : Westerski, Adam. Semantic Technologies In Idea Management Systems: A Model for Interoperability, Linking & Filtering. Thesis. Universidad Politecnica De Madrid/ Madrid, 2013. N.p.: Google, 2013. Print.

(Sitaram/Bernardo 2010) : Asur, Sitaram, and Bernardo A.Huberman. Predicting the Future With Social Media. Tech. N.p.: n.p., 2010. Print

06 - Jun -14 32