I Understand What You Are Saying: Leveraging Deep Learning Techniques for Aspect Based Sentiment Analysis

Despite widespread use of online reviews in consumer purchase decision making, the potential value of online reviews in facilitating digital collaboration among product/service providers, consumers, and online retailers remains under explored. One of the significant barriers to realizing the above potential lies in the difficulty of understanding online reviews due to their sheer volume and free-text form. To promote digital collaborations, we investigate aspect based sentiment dynamics of online reviews by proposing a semi-supervised, deep learning facilitated analytical pipeline. This method leverages deep learning techniques for text representation and classification. Additionally, building on previous studies that address aspect extraction and sentiment identification in isolation, we address both aspects and sentiments analyses simultaneously. Further, this study presents a novel perspective to understanding the dynamics of aspect based sentiments by analyzing aspect based sentiment in time series. The findings of this study have significant implications with regards to digital collaborations among consumers, product/service providers and other stakeholders of online reviews

Hybrid recommendation system using product reviews

Abstract. Several businesses/smart applications rely on personalizing their services to adapt to the user’s preferences. Personalized services are developed using recommendation systems based on user’s feedback on products/services, needs, habits and social or demographic characteristics. Several businesses from e-commerce (suggesting users what to buy) to hospitality services (suggesting which hotel to book) focus on using recommendation systems to achieve a personalized experience for their users. Majority of recommendation systems make use of only product ratings shared by the users, this may pose challenges like sparsity of ratings. The wide availability of other attributes of products or users like textual product reviews provided by users or product descriptions in e-commerce and hospitality domains present a gold mine of additional personalising information with which to supplement their ratings based recommendation system. Recommendation systems majorly involves two tasks: rating (predict ratings that user might assign to a product) and ranking (recommend products based on predicted rank scores) prediction tasks. In this thesis, we propose a novel hybrid recommendation system using the state-of-the-art DeepFM model which makes use of multiple textual features derived from product reviews particularly contextual sentence embedding vectors, average sentiment scores and linguistic cues such as presence/absence of negation in the product reviews in combination with ratings shared by users to enhance the prediction of the desired ratings or rank scores. We evaluated our system with commercial datasets from Amazon and Datafiniti for both tasks: predicting rating and recommendations based on predicted rank scores. We utilised different metrics for both types of tasks. From our evaluation we infer that using contextual sentence embedding vectors extracted using BERT, average sentiment scores and presence/absence of negation in the product reviews obtained from VADER, does impact the prediction of ratings and recommendations based on predicted scores of the recommendation system which only utilises product ratings as user preferences. Furthermore, we can conclude from our evaluation that (A) contextual embedding vectors and average sentiment scores together along with ratings in the proposed hybrid system improves prediction of desired ratings, (B) contextual embedding vectors, average sentiment scores and presence/absence of negation in the product reviews together along with ratings in the proposed hybrid system improves prediction of desired ratings as well, (C) contextual embedding vectors and average sentiment scores together along with ratings in the proposed hybrid system improves recommendations based on rank scores

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection