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A Survey of Word2vec Inversion Methods in Topic Recognition Tasks

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A Survey of Word2vec Inversion Methods in Topic Recognition Tasks A Survey of Word2Vec Inversion Methods in Topic Recognition Tasks Sarah Wiegreffe Paul Anderson, PhD Department of Computer Science Department of Computer Science College of Charleston College of Charleston 66 George St 66 George St Charleston, SC 29424 Charleston, SC 29424 [email protected] [email protected] Abstract number of features, but words and documents have variable numbers of characters and An extension of Google's word vec- words. Various methods such as Bag of tor embedding algorithm, Word2Vec Words have been developed to transform (Mikolov et al., 2013), Word2Vec In- variable-length documents into fixed-length version (Taddy, 2015a) is a simple and objects and subsequently use them in a robust technique that allows for clas- classification task. Issues with these methods sification tasks such as topic recogni- include a disregard for word order, similarity, tion of variable-length inputs includ- and importance (treating every word in a ing sentences and documents. The document with the same weight or documents method uses a simple averaging of containing the same words in different orders sentence scores to classify the doc- having the same representation, for example). ument, and demonstrates that perfor- Word embedding techniques such as mance is similar to other traditional Google’s Word2Vec algorithm proposed by methods, while better capturing word Mikolov et al. (2013) map each word in a similarity and importance. However, high-dimensional vector space to account for Word2Vec Inversion is sensitive to bias similarity, generating fixed-length distributed from strongly misclassified sentences representations that can be passed on as when factored into the average. In this input to a machine learning algorithm. Taddy paper an overview of various alterna- extends this technique by proposing a method tive methods for combining sentence called Word2Vec Inversion (2015a), which scores is presented, and their perfor- trains two topic-specific Word2Vec models mance compared to Word2Vec Inver- and uses a Bayesian likelihood score a new sion is reported, in an effort to better document receives from each model as the capture overall review classification in means by which it is classified, skipping the a way less sensitive to sentence bias. preprocessing step altogether. 1 Introduction Taddy’s implementation uses the Python gensim Word2Vec package (2015b), and Topic recognition of a text, such as deter- works by building a vocabulary and subse- mining the sentiment of a movie review, is a quently training a basemodel on all sentences popular topic of study in the field of natural in the training set, then training two separate, language processing, in many ways because class-specific copies of the basemodel using of the dissimilarity between the quirks of only the training data of that class (in this language and communication that express case, positive or negative sentiment) from a sentiment and the capabilities of compu- dataset of Yelp movie reviews. Each of the tational machines. Traditional machine two models scores a new (test) document with learning algorithms such as Random Forest the likelihood of it belonging to the model, require input data to have a fixed-length Figure 2: Probability Distribution of Positive Sentence Scores among Misclassified True Positive Reviews. Figure 1: A high-level overview of the Word2Vec Inversion algorithm. Figure 3: Probability Distribution of Negative and the document can thus be classified as Sentence Scores among Misclassified True belonging to the class for which it receives Negative Reviews. the highest likelihood score. However, the Word2Vec Inversion algorithm scores sentences rather than whole documents, so tain aspects of a film at the beginning of his or the two models actually provide a likelihood her review, but conclude with a feeling of am- score for each sentence in the review, which bivalent positivity towards it. A strong nega- are averaged to obtain a document likelihood tive first sentence such as this was observed to (see figure 1). have caused misclassification of the review in some instances. 2 Motivation Probability distributions were generated to visualize this trend (see figures 2 and 3). The motivation for this work comes from an By looking at the sentence score distribution analysis of movie reviews from the Kaggle among all misclassified reviews, useful infor- IMDb dataset (2014) (and parsed using the mation comes to light. For example, among Kaggle Word2Vec Utility (Kan, 2014)) mis- misclassified positive but true negative re- classified by Word2Vec Inversion in a basic views, almost two times more sentences were sentiment analysis task. It was determined assigned a 0 to 0.1 likelihood of being nega- that the most common characteristic shared by tive (and thus a 0.9 to 1.0 likelihood of being misclassified reviews (both positive and neg- positive) than a 0.9 to 1.0 likelihood. ative) is having one or a few misclassified sentences within a review that received very 3 Methods strong likelihood scores of being of the oppo- site sentiment of the review as a whole, skew- 3.1 Exploration ing the averaged likelihood. It was also ob- Initial alternative methods to sentence aver- served that sentences of an opposite sentiment aging developed in an effort to reduce sen- of the review as a whole were often located at tence bias include using the maximum and the beginning or end of the review. For ex- median sentence scores to classify. (Because ample, a movie reviewer might critique cer- the likelihoods assigned by the positive sen- timent model and the negative are normal- ized to sum to one in Taddy’s implementation (2015b), selecting the maximum score, min- imum score, maximum difference between positive and negative likelihood, or minimum difference, are all equal and effectively result in the same document score). Initial Results Accuracy scores from using the maximum sentence score (MaxProb) and median meth- ods on the Kaggle IMDb movie reviews with a simple training/testing split of 50/50 and 5- fold cross validation are found in Table 1. Per- formance improvements were not seen, rather performance remained about the same regard- less of technique. Figure 4: An overview of the Distribution After once again analyzing misclassified Features technique. reviews from the MaxProb method and the baseline averaging, it became evident that many reviews had sentences scoring very number of sentences in the review, sigmoidal highly (with greater than 0.9 likelihood) of be- approximation functions with k=10 and k=20, longing to both classes. Thus, selecting the and positions of the minimum and maximum maximum of the two was not valuable in de- scores in the review. These generated dis- termining the true relationship of that high tribution features were compiled as a fixed- scoring sentence to the remainder of the re- length feature vector of size 10 for each re- view and its overall sentiment. view. The feature vectors were then used to train decision tree (’DT’) and random forest 3.2 Development of the Random Forest (’RF’) learners (see figure 4). of Distribution Features Technique The methods were tested on three datasets: Based on how similar the results obtained IMDb movie reviews (imd, 2014), fine from the exploratory study were, we set out to food reviews from Amazon (McCauley and answer the hypothesis of whether employing Leskovec, 2013), and tweets taken from Twit- a machine learning algorithm to find the ideal ter (Naji, 2012). The Kaggle IMDb movie re- means of combining sentence scores could views had a size of 100,000 records, class la- improve prediction accuracy. Using machine bels of either positive or negative sentiment, learning to discern the most important fea- and were multiple sentences long on average. tures of the reviews or positions of the sen- The Twitter sentiment analysis dataset was tences instead of attempting to do so manu- randomly subsetted down to 99,999 records ally allows for generalization of the technique for computational feasibility and had the same to other topic recognition tasks. class labels, but the records were shorter in From the output of the Word2Vec Inver- length- many only parsed to one sentence. sion model, each sentence received a proba- The Amazon fine food reviews dataset (Mc- bility score from the negative model. Various Cauley and Leskovec, 2013) was multi-class, summary scores were generated from groups with labels of one to five star ratings assigned of these sentence scores on an individual re- by the writers of the reviews, and, follow- view level, including the mean, minimum, and ing a subsetting of the five-star class to make maximum scores, the standard deviation of the difference in class size more equal, had scores, scores of the first and last sentences, 255,332 records. Technique Mean (Baseline) MaxProb Median Score 0.8738 0.8551 0.8540 Table 1: Exploratory Method Results for Combining Sentence Scores for Kaggle’s IMDb Movie Reviews dataset. 4 Results 5 Conclusion Preliminary results support that the Decision Tables 2 through 6 illustrate various perfor- Tree and Random Forest with Distribution mance measures of the methods on the three Features techniques, which stemmed from a aforementioned datasets. The results are pre- goal of finding a more optimal means of com- sented as 95% confidence intervals generated bining sentence score output from Word2Vec from 25 runs of a 5 fold cross-validation loop Inversion into a classification, outperform for each model. For the multi-class Amazon baseline Word2Vec Inversion, but are not de- fine foods dataset, scores were calculated us- cisive. However, the results support that ing a one-versus-all approach for each class, Word2Vec Inversion should not be discred- and presented in the table as the average per- ited as a well-performing algorithm for clas- formance across all five class models. No sification. Completing other topic recogni- Word2Vec Inversion with a decision tree or tion tasks apart from sentiment analysis on random forest classifier scores are reported for datasets with a more diverse range of docu- this dataset, due to the 5-class scoring render- ment lengths and vocabularies, such as de- ing the task too computationally expensive.
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