STATEMENT REGARDING PRIOR DISCLOSURE BY THE INVENTORS

Aspects of this technology are described in an article “Open Domain Targeted Sentiment Classification Using Semi-Supervised Dynamic Generation of Feature Attributes” published in Journal of Universal Computer Science, Vol. 24, No. 11 (2018), 1582-1603, on Nov. 28, 2018, which is incorporated herein by reference in its entirety.

STATEMENT OF ACKNOWLEDGEMENT

The inventors acknowledge King Fahd University of Petroleum and Minerals (KFUPM) for providing the facilities to support this work.

BACKGROUND

Technical Field

The present disclosure is directed to open domain targeted sentiment classification using semi-supervised dynamic generation of feature attributes to determine topics in micro-blogs and identify sentiment expressed toward the topics.

Description of Related Art

The “background” description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description which may not otherwise qualify as prior art at the time of filing, are neither expressly or impliedly admitted as prior art against the present invention.

Sentiment analysis is area which has increased in importance with natural language processing (NLP). Sentiment analysis mines opinions that are included in text with the goal of discovering insights into human behavior and opinion. Sentiment analysis has been employed in numerous applications, such as consumer product marketing and political opinion mining. (See Chamlertwat, W., Bhattarakosol, P., Rungkasiri, T., Haruechaiyasak, C.: “Discovering Consumer Insight from Twitter via Sentiment Analysis”; Journal of Universal Computer Science, 18, 8 (2012) 973-992; See Liu, B.: “Sentiment Analysis: Mining Opinions, Sentiments, and Emotions”; Cambridge University Press (2015), each incorporated herein by reference in its entirety).

Micro-blogging is an online broadcast medium that exists as a specific form of blogging. A micro-blog differs from a traditional blog in that its content is typically smaller in both actual and aggregated file size. Micro-blogs allow users to exchange small elements of content such as short sentences, individual images, or video links. These small messages are sometimes called micro-posts. As with traditional blogging, micro-bloggers post about topics ranging from the simple, such as “what I'm doing right now,” to the thematic, such as “sports cars.” Commercial micro-blogs also exist to promote websites, services and products, and to promote collaboration within an organization. Micro-blogs may include text messaging, instant messaging, E-mail, or links to websites. Any kind of activity involving posting, whether on a social network site or a micro-blogging site, can be classified as micro-blogging (See Kaplan A., Haenlein M. (2011). “The early bird catches the news: Nine things you should know about micro-blogging” (PDF). Business Horizons, 54(2). March-April 2011; Aichner, T.; Jacob, F., “Measuring the Degree of Corporate Social Media Use”. International Journal of Market Research, 57 (2): 257-275. doi:10.2501/IJMR-2015-018, March 2015; Lohmann et al. (2012). “Visual Analysis of Microblog Content Using Time-Varying Co-occurrence Highlighting in Tag Clouds”, AVI 2012 Conference, each incorporated herein by reference in its entirety).

Microblogging services have been significantly increasing in number and activity and have enabled people to conveniently share their sentiments (opinions) with regard to matters of concerns. Such sentiments have shown an impact on many fields such as economics and politics. Different sentiment analysis approaches have been proposed in the literature to automatically predict sentiments shared in micro-blogs (e.g., tweets). A class of such approaches predicts opinion towards specific target (entity); this class is referred to as target-dependent sentiment classification. Another class, called open domain targeted sentiment classification, extracts targets from the micro-blog and predicts sentiment towards the target.

Many techniques have been proposed for detecting polarities expressed in micro-blogs. The more formal approach is based on identifying sentiment (opinion) that is expressed toward the whole micro-blog. This approach cannot detect more than one sentiment even if the micro-blog includes more than one topic (target). Some techniques manipulate a weakness in the target-independent approach by predicting sentiment toward a specific target included in the micro-blog, which is referred to as target-dependent sentiment classification.

For example, if a micro-blog is analyzed for the phrase “Concorde is better than Boeing for long trips” by using the target-independent approach, the predicated sentiment will be a “positive” sentiment since the micro-blog contains the positive phrase “better than”. While applying target-dependent sentiment classification will output “positive” sentiments if the interested target is “Concorde”, the output will be a “negative” sentiment when the requested target is “Boeing”.

Another approach first predicts the named entities (targets) in the micro-blog and then identifies sentiments toward them. Referring to the above example, the system will first detect the words “Concorde” and “Boeing” as targets and then identify sentiments toward them as discussed previously. This recent approach is referred to as open domain targeted sentiment analysis.

One approach to open domain targeted sentiment classification joined two tasks: named entity recognition (NER) and targeted sentiment classification. NER identified named entities (targets) in a micro-blog, while targeted sentiment classification predicted sentiment polarities toward identified targets. (See Mitchell, M., Aguilar, J., Wilson, T., Durme, B.: “Open domain targeted sentiment”; Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2013); Ratinov, L., Roth, D.: “Design Challenges and Misconceptions in Named Entity Recognition”; Proc. of the 13th Conference on Computational Natural Language Learning, Stroudsburg, Pa., (2009) 147-155, each incorporated herein by reference in its entirety).

Another approach uses sequence labelling, in which a sequence of words (tokens) that forms each micro-blog must be analyzed. To classify a sequence of words, a technique from neuro-linquistic programming (NLP) called “sequence labelling” is used. In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. A common example of a sequence labeling task is part of speech tagging, which seeks to assign a part of speech to each word in an input sentence or document. Sequence labeling can be treated as a set of independent classification tasks, one per member of the sequence. However, accuracy is generally improved by making the optimal label for a given element dependent on the choices of nearby elements, using special algorithms to choose the globally best set of labels for the entire sequence at once. (See Nguyen, N., Guo, Y. “Comparisons of Sequence Labeling Algorithms and Extensions”; Proc. of the 24th International Conference on Machine Learning (2007) 681-688, incorporated herein by reference in its entirety).

Approaches similar to open domain targeted sentiment analysis are based on aspect-oriented sentiment analysis which extracts product attributes from user reviews and predicts opinions towards them. The difference between “target” and “aspect” is that “target” refers to an independent topic (entity) included in the micro-blog. The micro-blog may include more than one target. An “aspect” refers to a subtopic that is related to a main topic. For example, the micro-blogger may talk about features that are related to a specific product. Thus, all features are detected as aspects. (See Hu, M., Liu, B.: “Mining and summarizing customer reviews”; Proc. of SIGKDD (2004) 168-177; and Popescu, A., Etzioni, O.: “Extracting product features and opinions from reviews”; In Natural language processing and text mining (2007), each incorporated herein by reference in their entirety). Moreover a topic-oriented sentiment analysis for extracting features and sentiments towards certain topics has conventionally been employed. (See Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: “Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach”; In CIKM (2011) 1031-1040, incorporated herein by reference in its entirety).

Word embeddings may improve the accuracy of open domain targeted sentiment classification. (See Mikolov, T., Chen, K., Corrado, G., Dean, J.: “Efficient estimation of word representations in vector space”; Proc. of International Conference on Learning Representations (2013), incorporated herein by reference in its entirety). Word embeddings is a technique of substituting each token (word) in a micro-blog by a numerical vector. Word embeddings preserve the similarity between similar words in meaning. Thus, word embeddings convert words to vectors while the similarity between vectors mimics semantic similarity between words. As a result, the technique of using word embeddings has improved the performance of many applications used in deep learning and NLP.

A semi-supervised learning technique for open domain targeted sentiment classification has not previously been explored. (See Abudalfa, S., Ahmed, M.: “Survey on Target Dependent Sentiment Analysis of Micro-Blogs in Social Media”; Proc. 9th IEEE GCC Conference & Exhibition (2017), incorporated herein by reference in its entirety). The use of a supervised learning technique for open domain targeted sentiment analysis requires a large amount of labelled data during the process of training models. Thus, applying a supervised learning technique to labelled micro-blogs is a difficult task since annotating micro-blogs is a time consuming process and is subject human error. However, automated systems for annotating micro-blogs have been found to yield inaccurate results. Additionally, using the open domain targeted sentiment approach compounds the problem, since labels must be provided for both NER and sentiment prediction.

Accordingly, it is one object of the present disclosure to provide methods and systems for sentiment analysis based on semi-supervised learning which provides open domain targeted sentiment classification using less labelled data, thus decreasing processing costs. Additionally, a supervised learning model for improving the accuracy of open domain targeted sentiment classification is presented using SVM HMM.

Results of experiments demonstrate that semi-supervised based open domain targeted sentiment classification improves the performance of named entity recognition and sentiment prediction for both English and Spanish micro-blogs.

SUMMARY

In an exemplary embodiment, a semi-supervised learning method for generating feature attributes in open domain targeted sentiment classification of a dataset of microblogs is described as comprising training, with a computing system having circuitry configured for modeling and processing, a hidden Markov model support vector machine (SVM HMM) by the steps of dynamically generating feature attributes by combining pre-trained word embeddings from a plurality of different sources to form a first training dataset, normalizing the first training dataset to generate a normalized training dataset, concatenating, with a summer, discrete features with the normalized training set to form a second training dataset. Dynamically generating feature attributes includes selecting a subset of the training dataset, applying k-means clustering to the subset, determining the cluster IDs for the training dataset, normalizing the cluster IDs and concatenating the normalized cluster IDs as additional attributes with the second dataset for forming a third dataset and formatting the third dataset. The method further includes initializing a C parameter, applying the second training dataset with the C parameter to the SVM HMM, and training the SVM HMM to classify the second training dataset set. The training includes optimizing the initialization of the C parameter by replacing the C parameter with incrementally increased values. The method further includes identifying topics and sentiment polarities within a dataset of micro-blogs by concatenating the generated attributes with word embeddings included in the dataset of micro-blogs, normalizing the word embedded dataset of micro-blogs, formatting the normalized word embedded dataset of micro-blogs to form a formatted dataset, classifying the formatted dataset by applying the formatted dataset to the trained SVM HMM, and outputting a sentiment analysis (detected topics and identified sentiments expressed toward the topics) of the dataset of micro-blogs.

In another exemplary embodiment, a non-transitory computer readable medium having instructions stored therein that, when executed by one or more processor, cause the one or more processors to perform a semi-supervised learning method comprising training a SVM HMM model to classify micro-blogs for open domain targeted sentiment analysis.

The foregoing general description of the illustrative embodiments and the following detailed description thereof are merely exemplary aspects of the teachings of this disclosure, and are not restrictive.

