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A Document-Level Sentiment Analysis Approach Using Artificial Neural Network and Sentiment Lexicons Yan Zhu.

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Presentation on theme: "A Document-Level Sentiment Analysis Approach Using Artificial Neural Network and Sentiment Lexicons Yan Zhu."— Presentation transcript:

1 A Document-Level Sentiment Analysis Approach Using Artificial Neural Network and Sentiment Lexicons Yan Zhu

2 Agenda  Overview  Objective  The Proposed Method  Experiment and Result  Conclusion

3 Overview  Online consumers can use various Web 2.0 mediums like message forums, blogs or reviews sites to express their opinion and access opinions expressed by others  Research has confirmed that online reviews by consumers can be a good proxy for word-of-mouth, and can ultimately influence the purchase decision-making of other potential buyers, who explore the Internet for product related information

4 Overview  Sentiment Analysis or opinion mining is a recent research area where we attempt to classify opinionated texts according to their polarity (positive or negative).  Different solutions have been proposed for sentiment analysis of online text data, including those based on machine learning, dictionary, statistical, and semantic approaches

5 Objective  In this paper, we have proposed a sentiment classification model using back-propagation artificial neural network (BPANN).  Information Gain and three popular sentiment lexicons have been used to extract sentiment representing features.  The proposed approach exploits classification performance of BPANN and utilizes domain knowledge from the sentiment lexicons for document-level sentiment analysis

6 Need of BPANN for Sentiment Analysis  Machine learning techniques have performed better in terms of accuracy than semantic and lexicon based methods of sentiment analysis  However, the performance of machine learning approaches is heavily dependent on the selected features, the quality and quantity of training data and the domain of the dataset

7 Need of BPANN for Sentiment Analysis  The additional learning time required by the machine learning techniques is also a prominent issue, since lexicon or semantic based approaches do not need time for training.  The additional learning time required by the machine learning techniques is also a prominent issue, since lexicon or semantic based approaches do not need time for training

8 The Proposed Method  The opinionated text documents are collected and then, preprocessed.  The Vector Space Model (VSM) is utilized in order to generate the bag of words representation for each document  Stemming is done to reduce words to their basic root or stem  Stop words are discarded, but we have consciously preserved some useful sentiment expressing terms such as “ok” and “not”.

9 The Proposed Method  To compute a numerical representation from user-generated opinionated text data, the residual tokens are arranged as per their frequencies or occurrences in whole documents set.  Information gain based feature selection is effective for sentiment based text classification. Hence, the top n-ranked features are selected based upon information gain feature selection and the most frequent words in sentiment lexicons.

10 Information Gain Feature Selection  Information gain (IG) is a feature goodness criterion and has performed well for sentiment-feature selection.  A feature is selected based on its impact on decreasing overall entropy.  The attributes ranked high as per IG score will minimize the overall information necessary to classify instances into predefined classes

11 Information Gain Feature Selection  The information gain of a feature w, over all classes is given by  P(ci): probability that a random instance document belongs to class ci.  P(w): probability of the occurrence of the feature w in a randomly selected document.  P(ci |w): probability that a randomly selected document belongs to class ci if document has the feature w.

12 Back-Propagation Artificial Neural Network (BPANN)  Artificial Neural network (ANN) is a classifier that can be adopted for linear and non-linear text categorization problems.  Advantages: adaptive learning, parallelism, pattern learning, sequence recognition, fault tolerance, and generalization.  ANNs can be feed-forward or feedback networks

13 Back-Propagation Artificial Neural Network (BPANN)  Among the various training algorithms of ANN, error back propagation (BP) is the best known algorithm  BP is an iterative gradient algorithm proposed to minimize the mean square error (MSE) which is MSE a measure of difference between actual and desired output of a multilayer feed-forward neural network

14 Back-Propagation Artificial Neural Network (BPANN)  The BP algorithm works in two passes:  Forward pass obtains the inputs and activation value  Backward pass is performed to adjust the weights of network nodes and minimize the MSE.  These two passes will repeat iteratively until the network converges.

15 Back-Propagation Artificial Neural Network (BPANN)

16  BPANN can be represented by a network diagram formed by connected nodes using directed edges and arranged in different layers.  Each layer of BPANN consists of processing elements.  All neurons interact though weighted connections. The associated weight value with each directed link is determined by minimizing a global error function through backward error propagation in a gradient descent learning process.

17 Back-Propagation Artificial Neural Network (BPANN)  In forward pass, a neuron computes a weighted sum of the sample inputs applied and then perform an activation function to this sum to calculate its output.  The input signal will gradually progress through each layer using the non- linear function. The sigmoid transfer function is a popular activation function  During training, the weights of each layer‘s neurons are modified to minimize the global error

18 Experimental Evaluation  Movie reviews dataset  1,000 positive and 1,000 negative reviews  Hotel reviews dataset  501 positive and 501 negative user-generated reviews

19 Experimental Evaluation  Three popular lexicons (sentiment dictionaries) have been used in this study.  HM dataset: 1336 adjectives: 657 positive and 679 negative  GI dataset: 3596 adjectives, adverbs, nouns, and verbs, out of which 1614 are positive and 1982 are negative  Opinion Lexicon: 2006 positive and 4783 negative words

20 Performance Evaluation  Overall accuracy (OA) as performance evaluation metrics  Precision and Recall

21 Experimental Design and Results  This study has used feed forward BPANN with single hidden layer.  The number of input nodes in input layer was set between 50 and 1000 as in prior research studies  The hidden nodes were kept as 15 and output nodes were set at 2, for binary classification.  Use 500 iterations with learning rate as 0.01 and momentum as 0.8 for BPANN  Use 66% of the dataset for training and 33% for testing the BPANN

22 Experimental Design and Results  The BPANN using gradient descent method often fails to converge.  This study involved selection of model parameter by training the candidate model multiple times (3 and more) by initializing with different set of randomly generated weights.  Thus, repeated model training with setting random initial weights is performed to overcome the non-convergence problem.  Restricted iterations as early stopping procedure to avoid risk of overfitting

23 Movie Result

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26 Hotel Result

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29 Results  The model complexity of BPANN is controlled by restricting the number of hidden layers (usually, just one) and number of neurons in each hidden layer  Selecting more than 1000 features based on sentiment lexicons and information gain did not lead to improvement in classification results.  BPANN can be sensitive to total number of training and test instances provided when experiment with different sized datasets

30 Conclusion  The results clearly indicate that BPANN is suitable for sentiment based classification  Information Gain (IG) has out-performed lexicon based feature selection and succeeded in reducing the dimensionality


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