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Students will get an overview about the area of sentiment analysis and the challenges it presents. Students will read scientific papers and familiarize themselves with this kind of literature.
Note: The papers that will be read this year are different from those read last year, students from last year are welcome to join.
Please also note: That obviously you cannot get credit twice for the same class even though you do a different presentation.
| Day | Topic | Presenter | Material* |
| Tuesday 10.4. | Introduction to sentiment analysis | Wiltrud Kessler | Slides [Liu10] |
| Tuesday 17.4. | Seminar topics distribution How to find literature |
Wiltrud Kessler | Slides |
| Tuesday 24.4. | Sentiment polarity and polarity modifiers | Wiltrud Kessler | Slides |
| Tuesday 1.5. | No class (public holiday) | ||
| Tuesday 8.5. | Evaluation of supervised text classification | Wiltrud Kessler | Slides Java Code WEKA |
| Tuesday 15.5. | Automatically determining word polarity Turney: "Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews" |
Wiltrud Kessler | [Tu02] Slides |
| Tuesday 22.5. | Linguistic Features Matsumoto, Takamura, and Okumura: "Sentiment classification using word sub-sequences and dependency sub-trees" |
Bernadette | [MTO05] |
| Tuesday 29.5. | No class (Pfingstferien) | ||
| Tuesday 5.6. | Subjectivy Classification Wilson, Wiebe and Hoffmann: "Recognizing contextual polarity in phrase-level sentiment analysis" (Concentrate on 'neutral-polar classification') |
Wiltrud Kessler | [WWH05] Slides MPQA Sentiment Resources |
| Tuesday 12.6. | Subjectivity Word Sense Disambiguation Akkaya, Wiebe and Milhacea: "Subjectivity word sense disambiguation" (Concentrate on section 3) |
Maxim | [AWM09] |
| Tuesday 19.6. | Comparative Sentences Jindal and Liu: "Identifying comparative sentences in text documents" |
Cornelia | [JL06a] |
| Tuesday 26.6. | No class | ||
| Tuesday 3.7. | Opinion Spam Ott, Choi, Cardie, and Hancock: "Finding deceptive opinion spam by any stretch of the imagination" |
Jonathan | [OCCH11] |
| Tuesday 10.7. | Conditional Sentences Narayanan, Liu, and Choudhary: "Sentiment analysis of conditional sentences" |
Vitalia | [NLC09] |
| Tuesday 17.7. | Polarity Reversers Ikeda, Takamura, Ratinov, and Okumura: "Learning to shift the polarity of words for sentiment classification" (Concentrate on 'word-wise learning') |
Melanie | [ITRO08] |
Sentiment analysis automatically identifies opinions expressed in language about real-world items. Most commonly, opinions are classified into the categories "positive" and "negative". Sentiment analysis has become an important topic over the last 10 years and there has been a large amount of publications in this area. In this seminar different methods for analyzing opinions on different levels will be presented.
Subjective statements refer to the internal state of mind of a person and cannot be observed. In contrast, objective statements can be verified by observing and checking reality. It is sometimes useful for a sentiment analysis system to filter out objective language and predict sentiment based on subjective language only. Unfortunately, detecting subjectivity is also a complicated problem.
References: [RW03], [WWH05]Sentiment analysis often uses dictionaries that list the polarity of each word. However, many words have both subjective and objective senses. Subjective words used in an objective sense are a significant source of error in sentiment classification. Subjectivity word sense disambiguation tries to automatically determine which word instances in a corpus are being used with objective senses.
References: [WM06], [AWM09], [AWCM11]To determine the polarity of an expression with only a lexicon of positive and negative words is often not sufficient, because many phenomena can influence the polarity. The most obvious example for such influences are "polarity reversers", words that reverse the polarity of a sentiment word, e.g., "no" or "not". An approach to tackle this problem is to assume the polarity of a word is known and classify each sentiment word as reversed or non-reversed according to its context.
References: [ITRO08], [CC08], [WBRK10]Conditional sentences are sentences that describe implications or hypothtical situations and their consequences. Some conditional sentences directly express sentiment on a product, but many of them express a hypothetical situation, a wish or a general implication.
References: [NLC09]A common way to express opinions is by comparing one entity with a different entity. There are different types of comparisons, direct comparisons of two entities, a comparison of the entity to a general standard and superlatives that set one entity above all others in the comparison set. Simply detecting comparative adverbs or adjectives is not sufficient, because it is possible for a sentence to contains a comparative word, although it is not a comparative sentence ("couldn't agree with you more") while on the other hand a comparative sentence does not necessarily have to include any comparative word ("no joy stick unlike the sony ericsson t60").
References: [JL06a], [JL06b], [GL08]These papers present a framework for extracting the ratable aspects of objects from online user reviews. A statistical model is used to discover topics in text and extract text snippets supporting the ratings of aspect different aspects.
References: [TM08a], [TM08b]Many classifiers for the classification of sentiment polarity use only shallow features like bag-of-words. To enhance the accuracy of sentiment polarity classification, several features based on linguistic analysis and syntactic structures have been proposed.
References: [DLP03], [Ga04], [MTO05]The term "opinion spam" refers to fictive reviews that have been written to mislead humans or automatic systems in their evaluation of the opinions about a product or a service. Fictive positive reviews are written to artificially improve the perceived opinion of a product or a service, fictive negative reviews are written to damage the reputation of a competitor or its products.
References: [JL07], [JL08], [OCCH11]This course includes a number of introductory classes about the basics of sentiment analysis and the most important challenges in the area. Afterwards, some specific challenges for automatic sentiment analysis are presented in talks by the students. To get credit for this class, you need to give a presentation and hand in a written report about one of the topics presented above. Every student is required to read all papers to be discussed in class beforehand.
Some previous knowledge of machine learning methods may be helpful (e.g., from the class "statistische Sprachverarbeitung" or "Information Retrieval").
To get credit for this class, you need to give a presentation and hand in a written report about one of the topics presented above. The grade consist of the following parts:
Submissions will be managed in ILIAS.
Template for LaTeX:
LaTeX main file,
example bib file,
bibtex style file,
EACL style file,
lingmacros style file (you may not need this if it is already installed on your computer).
The compiled LaTeX file with a lot of useful hints (please have a look at this even if you are using Word):
pdf
Template for Word (I don't have Word, so these are the original EACL 2012 files, please ignore the instructions there and have a look at the compiled LaTeX file linked above for hints):
Word document,
style file.
A very quick guide to writing your report in LaTeX:
Download the files linked above. Put all of them in one folder. Rename the .tex file to ausarbeitungYOURNAME.tex. Open a terminal, go to that folder and type pdflatex ausarbeitungYOURNAME.tex. After a lot of printing on the command line, you should get a file named ausarbeitungYOURNAME.pdf. Voila, you did it!
Read through the things in ausarbeitungTemplate.tex, it contains examples for writing in italics, bold, creating tables, figures and references. Just copy what you need. Also, there are many many resources online, e.g. the LaTeX Wikibook.
If you get an error like ! LaTeX Error: File 'XYZ.sty' not found. make sure the file is in the same folder. If it is a file with .sty, it is a package. You have two possibilities, (a) remove the line \usepackage{XYZ} (which might cause some commands not to work or some things to look differently), or (b) try to download that file from CTAN and put it into your folder (it might be more complicated).
If you get a warning LaTeX Warning: There were undefined references. you will notice some ?? in your document at places where references should be. For references to sections, tables of figures, just run pdflatex ausarbeitungYOURNAME.tex again. For bibliography references you need to run bibtex ausarbeitungYOURNAME and then run pdflatex ausarbeitungYOURNAME.tex again twice.
If you get a warning LaTeX Warning: Label(s) may have changed. Rerun to get cross-references right. some references may be wrong (e.g. section 3 has changed to be now section 4, but your reference still says "see section 3"). Rerun pdflatex ausarbeitungYOURNAME.tex to get them right.
Very important: Before you hand in, make sure none of these warnings appear!
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