User and product information associated with a review is useful for sentiment polarity prediction. Typical approaches incorporating such information focus on modeling users and products as implicitly learned representation vectors. Most do not exploit the potential of historical reviews, or those that currently do require unnecessary modifications to model architecture or do not make full use of user/product associations. The contribution of this work is twofold: i) a method to explicitly employ historical reviews belonging to the same user/product to initialize representations, and ii) efficient incorporation of textual associations between users and products via a user-product cross-context module. Experiments on IMDb, Yelp-2013 and Yelp-2014 benchmarks show that our approach substantially outperforms previous state-of-the-art. Since we employ BERT-base as the encoder, we additionally provide experiments in which our approach performs well with Span-BERT and Longformer. Furthermore, experiments where the reviews of each user/product in the training data are downsampled demonstrate the effectiveness of our approach under a low-resource setting.

Fake review identification is an important topic and has gained the interest of experts all around the world. Identifying fake reviews is challenging for researchers, and there are several primary challenges to fake review detection. We propose developing an initial research paper for investigating fake reviews by using sentiment analysis. Ten research papers are identified that show fake reviews, and they discuss currently available solutions for predicting or detecting fake reviews. They also show the distribution of fake and truthful reviews through the analysis of sentiment. We summarize and compare previous studies related to fake reviews. We highlight the most significant challenges in the sentiment evaluation process and demonstrate that there is a significant impact on sentiment scores used to identify fake feedback.

Check out all the on-demand sessions from the Intelligent Security Summit here. With tech talent in short supply, companies are desperate to hold onto top performers. However, many are losing ground. Employees are sticking around for much shorter periods than they used to. Sentiment analysis combined with artificial intelligence (AI) is being harnessed to help companies in a number of ways: Discovering how employees feel about their work environment, how effective they feel training and skill development initiatives are, and what their concerns are, and how to spot danger signs, identify signs of burnout, identify indicators of job dissatisfaction, and prevent employees from jumping ship rivals.

The COVID-19 pandemic has caused drastic alternations in human's life in all aspects. The government's laws in this regard affected the lifestyle of all people. Due to this fact studying about the sentiment of individuals is important to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed a NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798 and F1score: 0.804.

Humans are skilled in reading the interlocutor's emotion from multimodal signals, including spoken words, simultaneous speech, and facial expressions. It is still a challenge to effectively decode emotions from the complex interactions of multimodal signals. In this paper, we design three kinds of multimodal latent representations to refine the emotion analysis process and capture complex multimodal interactions from different views, including a intact three-modal integrating representation, a modality-shared representation, and three modality-individual representations. Then, a modality-semantic hierarchical fusion is proposed to reasonably incorporate these representations into a comprehensive interaction representation. The experimental results demonstrate that our EffMulti outperforms the state-of-the-art methods. The compelling performance benefits from its well-designed framework with ease of implementation, lower computing complexity, and less trainable parameters.

It is well-known that a sentiment analysis model determines whether a text is positive, negative, or neutral. However, this process typically requires access to unencrypted text, which can pose privacy concerns. Homomorphic encryption is a type of encryption that allows for computation on encrypted data without needing to decrypt it first. This makes it well-suited for applications where user's personal and potentially sensitive data is at risk (e.g. This blog post uses the Concrete-ML library, allowing data scientists to use machine learning models in fully homomorphic encryption (FHE) settings without any prior knowledge of cryptography.

This paper presents a cross-lingual sentiment analysis of news articles using zero-shot and few-shot learning. The study aims to classify the Croatian news articles with positive, negative, and neutral sentiments using the Slovene dataset. The system is based on a trilingual BERT-based model trained in three languages: English, Slovene, Croatian. The paper analyses different setups using datasets in two languages and proposes a simple multi-task model to perform sentiment classification. The evaluation is performed using the few-shot and zero-shot scenarios in single-task and multi-task experiments for Croatian and Slovene.

Sentiment analysis is a type of NLP that aims to label data according to its sentiments, such as positive, negative, and neutral. This analysis helps companies understand how their customers feel about their products or services or identify trends in public opinion about a particular topic. For example, a company like Audi can learn whether people like the colors of its new car by examining Twitter shares like the image below. With the developing technology, it is now much easier to express all kinds of emotions, feelings, and thoughts through social networking sites. Social media scraping is the process of extracting data from social media platforms.

Earthquakes have a deep impact on wide areas, and emergency rescue operations may benefit from social media information about the scope and extent of the disaster. Therefore, this work presents a text miningbased approach to collect and analyze social media data for early earthquake impact analysis. First, disasterrelated microblogs are collected from the Sina microblog based on crawler technology. Then, after data cleaning a series of analyses are conducted including (1) the hot words analysis, (2) the trend of the number of microblogs, (3) the trend of public opinion sentiment, and (4) a keyword and rule-based text classification for earthquake impact analysis. Finally, two recent earthquakes with the same magnitude and focal depth in China are analyzed to compare their impacts. The results show that the public opinion trend analysis and the trend of public opinion sentiment can estimate the earthquake's social impact at an early stage, which will be helpful to decision-making and rescue management.

Information extraction from scholarly articles is a challenging task due to the sizable document length and implicit information hidden in text, figures, and citations. Scholarly information extraction has various applications in exploration, archival, and curation services for digital libraries and knowledge management systems. We present MORTY, an information extraction technique that creates structured summaries of text from scholarly articles. Our approach condenses the article's full-text to property-value pairs as a segmented text snippet called structured summary. We also present a sizable scholarly dataset combining structured summaries retrieved from a scholarly knowledge graph and corresponding publicly available scientific articles, which we openly publish as a resource for the research community. Our results show that structured summarization is a suitable approach for targeted information extraction that complements other commonly used methods such as question answering and named entity recognition.