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

Computational Linguistics Volume 37, Number 2
different approach, instead supposing that negative expressions, being relatively rare,
are given more cognitive weight when they do appear. Thus we increase the final SO
of any negative expression (after other modifiers have applied) by a fixed amount
(currently 50%). This seems to have essentially the same effect in our experiments, and
is more theoretically satisfying.
Pang, Lee, and Vaithyanathan (2002) found that their machine-learning classifier
performed better when a binary feature was used indicating the presence of a unigram
in the text, instead of a numerical feature indicating the number of appearances. Counting
each word only once does not seem to work equally well for word-counting models.
We have, however, improved overall performance by decreasing the weight of words
that appear more often in the text: The nth appearance of a word in the text will have
only 1/n of its full SO value. 17 Consider the following invented example.
(17) Overall, the film was excellent. The acting was excellent, the plot was excellent,
and the direction was just plain excellent.
Pragmatically, the repetition of excellent suggests that the writer lacks additional substantive
commentary, and is simply using a generic positive word. We could also impose
an upper limit on the distance of repetitions, and decrease the weight only when they
appear close to each other. Repetitive weighting does not apply to words that have
been intensified, the rationale being that the purpose of the intensifier is to draw special
attention to them.
Another reason to tone down words that appear often in a text is that a word that
appears regularly is more likely to have a neutral sense. This is particularly true of
nouns. In one example from our corpus, the words death, turmoil, andwar each appear
twice. A single use of any of these words might indicate a comment (e.g., Iwasboredto
death), but repeated use suggests a descriptive narrative.
2.7 Other Features of SO-CAL
Two other features merit discussion: weighting and multiple cut-offs. First of all, SO-
CAL incorporates an option to assign different weights to sentences or portions of a text.
Taboada and Grieve (2004) improved performance of an earlier version of the SO calculator
by assigning the most weight at the two-thirds mark of a text, and significantly
less at the beginning. The current version has a user-configurable form of this weighting
system, allowing any span of the text (with the end points represented by fractions of
the entire text) to be given a certain weight. An even more flexible and powerful system
is provided by the XML weighting option. When this option is enabled, XML tag pairs in
the text (e.g., , ) can be used as a signal to the calculator that any words
appearing between these tags should be multiplied by a certain given weight. This gives
SO-CAL an interface to outside modules. For example, one module could pre-process
the text and tag spans that are believed to be topic sentences, another module could
provide discourse information such as rhetorical relations (Mann and Thompson 1988),
and a third module could label the sentences that seem to be subjective. Armed with
this information, SO-CAL can disregard or de-emphasize parts of the text that are less
relevant to sentiment analysis. This weighting feature is used in Taboada, Brooke, and
17 One of the reviewers points out that this is similar to the use of term frequency (tf-idf) in information
retrieval (Salton and McGill 1983). See also Paltoglou and Thelwall (2010) for a use of information
retrieval techniques in sentiment analysis.
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