Lexicon-Based Methods for Sentiment Analysis - Simon Fraser ...

Computational Linguistics Volume 37, Number 2
corpus), including a word in the analysis only if it appeared at least five times in both
corpora. That gave us a collection of 483 commonly occurring adjectives; these, as well
as our intensifier dictionary, were the focus of our evaluation. We also investigated a
set of nouns chosen using the same rubric. In most cases, the results were comparable
to those for adjectives; however, with a smaller test set (only 184 words), the data were
generally messier, and so we omit the details here.
The basic premise behind our evaluation technique can be described as follows: The
distributional spread of answers in a simple, three-way decision task should directly
reflect the relative distance of words on an SO (semantic orientation) scale. In particular,
we can validate our dictionaries without forcing Turkers to use our 11-point scale
(including zero), making their task significantly less onerous as well as less subject to
individual bias. We chose to derive the data in two ways: one task where the goal is to
decide whether a word is positive, negative, or neutral; and another where two polar
words are presented for comparison (Is one stronger, or are they the same strength?). In
the former task, we would expect to see more “errors,” that is, cases where a polar term
in our dictionary is labeled neutral, in words that are in fact more neutral (SO value 1
versus SO value 5). Similarly, in the second task we would expect to see more “equal”
judgments of words which are only 1 SO unit apart than those which are 3 SO units
apart. More formally, we predict that a good ranking of words subjected to this testing
should have the following characteristics:
The percentage of “equal/neutral” judgments should be maximized at
exactly the point we would expect, given the SO value assigned to the
word(s) in question. The number of polar responses should be relatively
balanced.
As we move away from this point (SO = 0) in either direction, we would
expect a linear increase in the appropriate polar response, and a
corresponding drop in the other responses.
The percentage of polar responses may hit a maximum, after which we
would expect a consistent distribution (within a small margin of error; this
maximum might not be 100%, due to noise in the data).
For the first task, that is, the neutral/negative/positive single word decision task,
we included neutral words from our Epinions corpus which had originally been excluded
from our dictionaries. Purely random sampling would have resulted in very
little data from the high SO end of the spectrum, so we randomly selected first by SO
(neutral SO = 0) and then by word, re-sampling from the first step if we had randomly
selected a word that had been used before. In the end, we tested 400 adjectives chosen
using this method. For each word, we solicited six judgments through Mechanical Turk.
Preparing data for the word comparison task was slightly more involved, because
we did not want to remove words from consideration just because they had been used
once. Note that we first segregated the words by polarity: We compared positive words
with positive words and negative words with negative words. This first test yielded a
nice wide range of comparisons by randomly selecting first by SO and then by word, as
well as by lowering the probability of picking high (absolute) SO words, and discounting
words which had been used recently (in early attempts we saw high absolute SO
words like great and terrible appearing over and over again, sometimes in consecutive
queries). Though the odds of this occurring were low, we explicitly disallowed duplicate
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