1. Sentiment Analysis

2. What is it?
Software for automatically extracting opinions, emotions and sentiments in text. It allows us to track attitudes and feelings on the web. People write blog posts, comments, reviews and tweets about all sorts of different topics. We can track products, brands and people for example and determine whether they are viewed positively or negatively on the web.

3. We can analyse...
acts: "The painting was more expensive than a Monet" pinions: "I honestly don't like Monet, Pollock is the better artist"

4. Why would we want to do this?
It allows businesses to track: - Flame detection (bad rants) - New product perception - Brand perception - Reputation management (Qnary) - Predict stock market returns?? It allows individuals to get: - An opinion on something (reviews) on a global scale

5. Several fields of computing merge
Natural language processing (NLP)
It deals with the actual text element. It transforms it into a format that the machine can use.
Artificial intelligence
It uses the information given by the NLP and uses a lot of math to determine whether something is negative or positive: it is used for clustering.

6. Challenges in Sentiment Analysis
1 - How does a machine define subjectivity & sentiment? 2 - How does a machine analyse polaraity (negative/positive)? 3 - How does a machine deal with subjective word senses? 4 - How does a machine assign an opinion rating? 5 - How does a machine know about sentiment intensity?

7. Different approaches and challenges
Sentiment and subjectivity classification. Two levels:
Classifying an opinionated document as expressing a positive or negative opinion
Classifying a sentence or a clause as subjective or objective. If subjective, classifying it as positive, negative, or neutral.
Feature-based sentiment analysis
Discover targets (product features,…) on which opinions have been expressed, and then are they positive, negative or neutral?

8. Different approaches and challenges
Sentiment analysis of comparative sentences
Which objects are preferred?
Practical challenges:
Define the competitive set;
Combine with feature analysis,…
Opinion search and retrieval
Opinion search engines. Combination search and sentiment analysis.
Two tasks:
Retrieve documents or sentences relevant to the query (e.g.,“gay marriage”)
Identify and rank opinionated documents or sentences from those retrieved.

9. What is an opinion?
"a personal belief or judgment that is not founded on proof or certainty" (WordNet) But: “The fact that an opinion has been widely held is no evidence whatever that it is not utterly absurd.” (Bertrand Russell) Word of mouth is powerful though...

10. It's not always easy to differentiate between fact and opinion.

11. What is an opinion to a machine?
It is a "quintuple", an object made up of 5 different things:
Oj = The thing in question (i.e product) 
f jk = a feature of Oj
SO ijkl = the sentiment value of the opinion of the opinion holder hi on feature fjk of object oj at time tl
These 5 elements have to be identified by the machine. Very hard to resolve by a computer.
{defined by Bing Liu in the NLP handbook}

12. Language is ambiguous
Consider: "The watch isn't water resistant" - In a product review this could be negative. "As much use as a trapdoor on a lifeboat" - negative but not obvious to the machine. "The canon camera is better than the Fisher Price one" - comparisons are hard to classify. "imo the ice cream is luuurrrrrrvely" - slang and the way we communicate in general needs to be processed.

13. The process...
1 - Part-of-speech tagging (but also position and more): The word in the text (or the sentence) is tagged using a POS-tagger so that it assigns a label to each word, allowing the machine to do something with it. It looks something like this:
S = subject
VP = Verb Phrase
V = Verb
N = Noun
NP = Noun Phrase
PP = Preposition
Det = Determiner
Then we extract defined patterns like [Det] + [NN] for example

14. The process
Classification based on Supervised Learning. Training and testing.
Features:
Terms and their frequency
Parts of speech tags
Opinion words and phrases
Syntactic dependency
Negation (be careful!: not only…. but…)
Caution: Domain specificity of language
Unpredictable plot vs. unpredictable steering
Classification based on Unsupervised Learning
Use certain words/phrases: the dictionary

15. The process part 2
We look at sentiment orientation (SO) of the patterns we extracted. For example we may have extracted: Amazing + Phone which is: [JJ] + [NN] (or adjective followed by noun in human) The opposite might be "Terrible" for example. In this stage, the machine tries to situate the words on an emotive scale (so to speak).

16. The process part 3
The average Sentiment orientation of all the phrases we gathered is computed. This allows the machine to say something like: "Generally people like the new iphone" --> They recommend it or "Generally people hate the new iphone" --> They don't recommend it

17. Features-Based Sentiment Analysis
Opinionated texts may write positive comments about a feature and negative comments about a different feature.
In spite of overall being positive, a text could contain negative opinions about a feature.
The quintuple becomes more complicated as features need to be also extracted.
Tasks:
Extract features
Determine whether the opinions on the features are positive, negative, or neutral.
Supervised training vs. unsupervised (large data set required: find NP,… using parts-of-speech taggers,…)

18. Sentiment Analysis of Comparative Sentences
Tasks:
Identify comparative/superlative sentences in the text and classify them into different types or classes
Type 1 (er/est) or Type 2 (more/most)
Increasing or decreasing
Type of comparative relations:
Non-equal gradable
Equative
Superlative
Non-gradable (“different from”,…)
Extract comparative opinions from the identified sentences
Extraction of features/objects can be done automatically.

19. Opinion Spam: The Bad and the Ugly
Human activities that try to deliberately mislead readers or automated opinion mining systems by giving:
Undeserving positive opinions to some target object
Unjust or false negative opinions to some other objects
Three types of spam reviews:
Type 1 (untruthful opinions): deliberate; to mislead readers or opinion mining systems.
Type 2 (opinions on brands only): not products; biased.
Type 3 (non-opinions): advertisements; questions; answers,…
Type 2 and type 3 easily found and dealt with.

20. Opinion Spam: The Bad and the Ugly
Finding Type 1 spam:
Outliers
Duplicate Reviews
Some findings. Spam tends to be:
Negative outlier reviews.
Only reviews of a product (n=1)
Top ranked reviewers are likely to be spammers
Review helpfulness scores are not helpful. Helpfulness scores can be spammed too!
Products of lower sale ranks are more likely to be spammed

21. So does it work?
The wider you throw the net and the more complex the language, the less accurate the system will be. This is simply due to the level of complexity it has to deal with. If you want to classifiy sentiments into +/- groups, then you are more likely to get a good result than if you are trying to classify into more exact groups (Excellent, incredible, good...). More granularity requires more accuracy and this in turn requires a deeper understanding of human language. There are commercial systems in place at this time and also systems like NaCTeM in the research space.

22. Things to read
Sentiment analysis in Text (SFS) Opinion mining and sentiment analysis (Bo Pang, Lillian Lee) Opinion Extraction, Summarization and Tracking in News and Blog Corpora (Ku, Liang, Chen) Sentiment analysis and subjectivity (Bing Liu) International sentiment analysis for news and blogs (Bautin) Sentiment analysis: does coreference matter? (Nikolov) CIKM Workshop on sentiment analysis

23. In the press
Google and sentiment analysis (SeoByTheSea) 5 ways sentiment analysis is ramping up in 2009 (RWW) Mining the web for feelings not facts (NY Times) Is sentiment analysis reliable? (Marketing Pilgrim) Sentimental searching (Watching the watchers)

24. Tools for coders
SentiWordNet LingPipe sentiment analysis Long list of tools at CodeSpeak The Toolkit for Advanced Discriminative Modeling (TADM) RapidMiner