Takamura et al. 2005

Citation

Hiroya Takamura, Takashi Inui, and Manabu Okumura. 2005. Extracting semantic orientations of words using spin model. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL '05).

Online version

ACL anthology

Summary

This paper follows many other sentiment analysis papers in analyzing graphs of words with synonym and antonym links to estimate the net sentiment of each word. Their estimation model, however, is a clear departure from most other work in NLP.

The fundamental idea of the paper is that sentiment of words occurring near each other (according to search engine hit counts) are likely to have similar sentiment values. They observe that this phenomenon is similar to the problem of determining the mostly likely spin states of each electron in a field of electrons.

As they describe it, on a local scale electrons near each other tend to have the same spin. To have two electrons near each other with differing spins requires some amount of energy, and as such, the goal of the optimization problem is to find the state of the electron field with the lowest possible energy. Fortunately, computational physicists have studied this spin model thoroughly. While exhaustive computation requires exponential time, they have also found tractable approximations.

Brief description of the method

The algorithm takes a written review as an input. First it assigns a POS tag to each word in the review to identify adjective or adverb phrases in the input review. They have used PMI-IR algorithm to estimate the semantic orientation of a phrase. The Pointwise Mutual Information (PMI) between two words $w_{1}$ and $w_{2}$ is defined as follow:

$PMI(w_{1},w_{2})=log_{2}(p(w_{1}\ and\ w_{2})/p(w_{1})p(w_{2}))$

where $p(w_{1},w_{2})$ is the probability that $w_{1}$ and $w_{2}$ co-occur. They have defined the semantic orientation of a phrase as follow:

$SO(phrase)=PMI(phrase,'excellent')-PMI(phrase,'poor')$

We can modify the above definition to obtain the following formula:

$SO(phrase)=log_{2}({\frac {hits(phrase\ NEAR\ 'excellent')hits('excellent')}{hits(phrase\ NEAR\ 'poor')hits('poor')}})$

where operator NEAR means that the two phrases should be appeared close to each other in the corpus. Using the above formula they have calculated the average semantic orientation for a review. They have shown that the value of average semantic orientation for phrases in the items that are tagged as "recommended" by the users are usually positive and those that are tagged as "not recommended" are usually negative.

Experimental Result

The approach described achieved accuracies from 75.2% using two seed words to 91.5% using leave-one-out cross validation. They compare their results to two previous methods for accomplishing the same task on a separate lexical graph constructed using only synonym connections. The first is the graph-based shortest-distance algorithm of Hu and Liu, which achieved a 70.8% accuracy, while Takamura et Al.'s approach achieved 73.4%. The second was Riloff et al.'s bootstrapping method which achieved 72.8%, compared to Takamura et al.'s 83.6% on that data set.