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| − | == Citation ==
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| − | Andrea Esuli, and Fabrizio Sebastiani. 2006. SENTIWORDNET: A Publicly Available Lexical Resource
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| − | for Opinion Mining. In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC '06).
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| − | == Online version ==
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| − | [http://gandalf.aksis.uib.no/lrec2006/pdf/384_pdf.pdf Universitetet i Bergen]
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| − | == Summary ==
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| − | This paper describes SentiWordNet, an expansion of the normal WordNet. For each SynSet (i.e. sense of a word) in WordNet, the authors compute a triplet of scores, obj(w), pos(w), and neg(w) representing the objectivity, positivity, and negativity, respectively of a word w. These score are each in the range [0.0, 1.0] and sum to 1.0 for any given SynSet.
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| − | They build SentiWordNet by creating an array of eight ternary classifiers. Each classifier is trained on a different data set using a different learning technique and outputs one of three categories: "objective", "positive" or "negative". Each SynSet is run through each classifier, and the three scores are computed by normalizing the number of classifiers that voted for each of the three categories.
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| − | Each classifier is trained in semi-supervised fashion using a seed vocabulary of 14 positive and negative terms, and another small set of objective words. The remaining data is classified with the classifiers, and normalized with other senses that WordNet calls either synonyms or antonyms to produce a new set of positive, negative, and objective training corpora.
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| − | == Brief description of the method ==
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| − | The method asserts that the 'energy' of an electron system <math>x</math> of <math>N</math> electrons is given by
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| − | <math>
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| − | E(x, w)=-\frac{1}{2}\sum_{i j}w_{i j} x_i x_j
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| − | </math>
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| − | where <math>x_i</math> is the spin (+1 or -1) of the <math>i</math>th electron and <math>w</math> is an <math>NxN</math> matrix representing the weights between each pair of electrons.
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| − | The probability of an electron configuration is given by
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| − | <math>
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| − | P(x|W) = \frac{1}{Z(W)} exp(-\Beta E(x, W))
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| − | </math>
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| − | where <math>Z(W)</math> is the normalization factor and <math>\Beta</math> is a hyper-parameter called the <i>inverse-temperature</i>.
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| − | Unfortunately, evaluating <math>Z(W)</math> is intractable, due to the fact that there are <math>2^N</math> possible configurations of electrons. As such Takamura et al. use a clever approximation. They seek a function <math>Q(\theta,W)</math> that is as similar to <math>P(x|W)</math> as possible. As a distance metric between the two functions they use the <i>variational free energy</i> <math>F(\theta)</math> which is defined as the difference between the mean energy with respect to <math>Q</math> and the entropy of <math>Q</math>.
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| − | This function's derivative is analytically findable, and hence given a starting value of <math>x</math> an analytic update rule can be found, and is shown in the paper.
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| − | They then require a way to compute the weighting table <math>W</math>. They do this by using their glossary of similar terms and defining <math>W_{i j} = \frac{1}{\sqrt{d(i) d(j)}}</math> where <math>d(i)</math> represents the degree of word <math>i</math>.
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| − | Finally, they discuss two methodologies for determining the hyper-parameter <math>\Beta</math>. The first is a simple leave-one-out error rate minimization method, as is standard in many machine learning problems. The second is physics-inspired and is called the <i>magnetism</i> of the system, defined by
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| − |
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| − | <math>
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| − | m = \frac{1}{N}\sum_i \bar{x_i}
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| − | </math>
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| − | They seek a value of <math>\Beta</math> that makes <math>m</math> positive, but as close as possible to zero. To accomplish this, they simply calculate <math>m</math> with several different values of <math>\Beta</math> and select the best one they find.
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| − | == Experimental Result ==
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| − | 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.
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| − | == Related papers ==
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