Cross-domain sentiment classification has been a hot spot these years, which aims to learn a reliable classifier using labeled data from the source domain and evaluate it on the target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: semantics attention network and domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains.

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research.

Since the beginning of the COVID pandemic, there have been more than 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus — and therefore some help from AI is highly needed. In this post, we will show how we...

In this first section, we will go through several general elements setting up the starting problem and the different challenges to overcome with text data. This is also the section in which we will discover our Twitter dataset, using libraries such as Pandas or Matplotlib.

Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well.

In the Pages application: marketers can measure the sentiment of the content they're producing. Microsoft Azure Text Sentiment Analysis interprets positive, neutral, and negative sentiment in real time. For example, "I like everything" will yield a high sentiment. Conversely, "I don't like anything" will yield a negative sentiment and "this is some text" will yield a neutral sentiment. This helps your marketing teams ensure that when they're creating content, the sentiment is in line with the context of the content strategy.

Sentiment analysis is the process of determining the opinion or feeling of a piece of text and determine whether the writer's attitude towards particular topic or product is positive, negative or neutral. The success of companies or products directly depends on customers. Once we know how the customer feels after analyzing their comments or reviews, they can identify what customer liked and disliked and built things like recommendation systems or more targeted marketing campaigns for them. IMDB dataset used for the project is a large movie review dataset. It consists of 50,000 movie reviews for natural language processing and text analytics.

Aspect-based sentiment classification aims to predict the sentiment polarity of a specific aspect in a sentence. However, most existing methods attempt to construct dependency relations into a homogeneous dependency graph with the sparsity and ambiguity, which cannot cover the comprehensive contextualized features of short texts or consider any additional node types or semantic relation information. To solve those issues, we present a sentiment analysis model named Isomer, which performs a dual-channel attention on heterogeneous dependency graphs incorporating external knowledge, to effectively integrate other additional information. Specifically, a transfer-enhanced dual-channel heterogeneous dependency attention network is devised in Isomer to model short texts using heterogeneous dependency graphs. These heterogeneous dependency graphs not only consider different types of information but also incorporate external knowledge. Experiments studies show that our model outperforms recent models on benchmark datasets. Furthermore, the results suggest that our method captures the importance of various information features to focus on informative contextual words.

In this webinar, the experts from Syskoplan Reply present the Machine Learning abilities of SAP Sales and Service Cloud. Get an overview of the newly added features and learn how to add value to your daily sales and service routines. Get familiar with Machine Learning functions such as Similar Ticket Recommendation, Email Template Recommendation and Machine Translation. The experts demonstrate how Natural Language Processing identifies product numbers, order numbers and language used from a customer's written text. Learn in a live demo how Ticket Categorization and Sentiment Analysis can save valuable time in ticket processing and see how additional valuable information is seamlessly integrated into SAP Service Cloud.

A satisfied customer is the life air of all successful businesses. Every business has one common goal in mind: how to continuously improve customer satisfaction, retain existing customers, and attract more customers. Therefore, customer feedback plays a pivotal role in helping a company understand its customer sentiment about a product and services. With the help of Natural Language Processing (NLP), a company can quickly perform sentiment analysis to gain insightful information about their customer behaviors, patterns and make recommendations. Sentiment Analysis is an extensive and influential topic in natural language processing (NLP) and Machine Learning(ML). Sentiment Analysis is the process of examining a piece of text for opinion and feeling.