Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and analyzing people’s opinions, sentiments, attitudes, perceptions, etc., toward different entities such as topics, products, and services. The fast evolution of Internet-based applications like websites, social networks, and blogs, leads people to generate enormous heaps of opinions and reviews about products, services, and day-to-day activities. Sentiment analysis poses as a powerful tool for businesses, governments, and researchers to extract and analyze public mood and views, gain business insight, and make better decisions. This paper presents a complete study of sentiment analysis approaches, challenges, and trends, to give researchers a global survey on sentiment analysis and its related fields. The paper presents the applications of sentiment analysis and describes the generic process of this task. Then, it reviews, compares, and investigates the used approaches to have an exhaustive view of their advantages and drawbacks. The challenges of sentiment analysis are discussed next to clarify future directions.

A comprehensive survey on sentiment analysis: Approaches, challenges and trends

The advent of Web 2.0 and its continuous growth has yielded enormous amounts of freely available user-generated information. Within this information, it is easy to find subjective texts, especially on social networks and eCommerce platforms that contain valuable information about users. Consequently, the field of opinion mining has attracted considerable interest over the last decade. Many new research articles are published every day, in which different artificial intelligence techniques (e.g., neural networks, fuzzy logic, clustering algorithms, and evolving computing) are applied to various tasks and applications related to opinion mining. Given this context, this survey presents a rigorous review of the different applications of fuzzy logic in opinion mining. The review portrays different uses of fuzzy logic and summarizes over one hundred and twenty articles published in the past decade regarding tasks and applications of opinion mining. This study is organized around three primary tasks, feature processing, review classification and emotions and also pays special attention to sentiment analysis applications whose core technique uses fuzzy logic to achieve the stated goals.

Fuzzy logic applied to opinion mining: A review

Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems

Recommender systems have become extremely common in recent years, and are applied in a variety of domains. Existing recommender systems exhibit two major limitations: (1) Privacy - each service provider holds a database that contains information about all of its users; and (2) Partial view - when recommending to users, each such service can rely only on data that were collected by the service itself. The Open Personal Data Store (openPDS) architecture was recently suggested for storing personal data in a privacy preserving way. Inspired by openPDS, we suggest a novel architecture for recommender systems that overcomes the two limitations mentioned above. The suggested architecture allows the recommender system to utilize rich data collected about the user (possibly through other services) to produce more accurate recommendations, while allowing its users to manage and gain control over their own data. We evaluate the suggested architecture on two different use cases: movies and web browsing, and compare its performance with that of a popular non-privacy-aware collaborative-filtering algorithm. We find that in comparison to the alternative approach, our approach is able to enhance privacy significantly without sacrificing the accuracy level of the recommendations (and in some cases providing even higher level of accuracy).

A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy

https://link.springer.com/content/pdf/10.1007/s12525-022-00537-z.pdf

Artificial intelligence in E-Commerce: a bibliometric study and literature review

New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests

The latest development in cognitive technologies are helping us understand emotions and sentiments with unprecedented precision. Polarity detection is the key enabler to sentiment analysis and typically relies on experimental dictionaries, where terms are assigned polarity scores, yet lacking contextual information and based on human inputs and conventions. In this article, we present a novel approach to automatically extract a polarity dictionary from a particular domain, the stock market, without human intervention and addressing the scaling and thresholding problem. Our approach tracks the price changes of particular stocks over time, using it as a guiding polarity value. The magnitude of the price variation for a particular stock is then attributed to the financial news about this stock in corresponding period of time and that is what we use as our working corpus. On top of that, we derive the so-called binned corpus and apply the well-known TF–IDF information retrieval techniques to compute the TF–IDF value for each term. These values are then disseminated within the neighbourhood of each term based on the embeddings-enabled cosine distance. After introducing the problem and providing the background information, we thoroughly describe our method and all the components required to implement the system. Last but not least, we assign the terms to fuzzy linguistic labels and provide a volatility metric indicating how reliable our scores are depending on their distribution of occurrences in the corpus. To show how our approach works, we implement it for the Euro Stoxx 50 from January 2018 to March 2019 and discuss the results compared with typical approaches, pointing out potential improvements for further research work.

Julio Herce-Zelaya

Papers

New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests

A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary

A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds

An automatic skills standardization method based on subject expert knowledge extraction and semantic matching

Adapting Recommender Systems to the New Data Privacy Regulations.