CS 8761 Final Project
Algorithm description

Alianza Lima

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Main idea
	We postulate the sentiment of a review can be obtained by
	examining the sentiment of the words used.
	
Main steps
	1. Use BigMac to filter out certain review words
	2. Attempt to determine sentiment using information in LDOCE
	3. Use the web to augment the information in LDOCE

Manual priming
	Our algorithm is primed by assigning a positive, negative,
	or neutral sentiment to each "Activ code" present in the 
	LDOCE dictionary. To be as objective as possible we only
	assigned a non-neutral sentiment when it was quite clear
	that the sentiment for a code should be so.

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More Detail

Step 1
	Each review's words are analyzed and filtered out according to
	their part-of-speech (POS) tag in BigMac. Our experimentation
	was done by filtering out everything except for adjectives.
	However, there is nothing precluding a person from loosening this
	level of filtering.

	A second type of filtering is available in the form of stop-words.

Step 2
	Once the review words are filtered out each one is examined to 
	determine it's sentimental impact on the overall review. Each
	word is then looked up in LDOCE for the senitment associated with
	each of it's activ codes. Each sense of a word can have a different
	code so we take the net sum of all the codes present for a word's
	contribution.

	In the event that a word is either not in LDOCE, or it has no activ
	codes available, we then use the web to determine it's sentimental
	impact. This is described in the next step.

Step 3
	In this stage we have found a word that has no sentimental information
	contained in LDOCE so we must create a sentiment for it. This is done
	by gathering a context in which the word is used and examining the
	sentiment of that context. To do this we query the web for a set of
	sentences containing the word, and then look-up each contextual word in 
	LDOCE like we tried for the original word. The resultant sum of sentiment 
	values becomes the sentiment for the original word.

Classifying
	The crudest method of classification is done by examining the sign of
	sentiment sum for the words in the review. If they sum to a positive
	number then the review is classified as positive. A negative sum results
	in a negative classification and a zero sentiment is undecided.


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Tweaks and alternate techniques

Scaling
	We propose a method of "tweaking" the sentimental impact of a word by
	examining where it occured in the original review. We propose three
	methods of performing this:
		
		1. Scale impact so words near the end of the review have the highest
			impact.
		2. Scale it so the beginning words have the most impact
		3. Scale so both beginning and end words have the most impact, but
			scale it so the middle words have reduced impact.

	The idea behind this is that it is a tendency of writing in general to
	summarize your intent in the end. Therefore, even a review that has
	positive points in the start, could be negative, and is shown by
	finishing the review with the negative reasons. The additional scaling
	options are more an experimental excercise, but should be interesting all
	the same.

Using an 'undecided' window
	The crude method of assigning the final sentiment above does not lend much
	chance of an undecided classification. For this reason we propose allowing
	a window of values dictate this assignment. While this will result in a
	loss of recall, due to less reviews being attempted, we feel an increase
	of precision will result from this threshold. 


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