In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

Sentiment Analysis is a vast and promising field in data analytics and trading. It is a rapidly rising type of analysis that uses the current pulse and market feeling to detect what participants intend to do or what positions they are holding.

… expressed in a text by a person could be understood by machine learning algorithms. … Let’s deep dive into these three negation types.

If the question'What is sentiment analysis?' popped up in your mind as you clicked on this blog, I think you will find my first blog in this series interesting. Essentially, sentiment analysis is a natural language processing technique used to determine the emotional tone of textual data. It is primarily used to understand customer satisfaction, and gauge brand reputation, call center interactions as well as customer feedback and messages. There are various types of sentiment analysis that are common in the real world. In this part of my blog series, let me walk you through the implementation of sentiment analysis.

Until now, most AI-related news reports have been related to image recognition and voice recognition, but with the evolution of AI, it is likely that there will be more reports and discussions on sentiment analysis AI in the future. In the United States, sentiment analysis AI that works on online conferencing systems has recently appeared one after another and has become a subject of controversy. For example, Silicon Valley startup Uniphore announced on March 1, 2022, the sentiment analysis AI "Q for Sales" aimed at supporting business negotiations . It is a sentiment analysis AI that uses computer vision, tonal analysis, conversation analysis, natural language processing, etc. It is said to read emotions from the facial expressions of the business partner and increase the business negotiation success rate.

"Multimodal sentiment analysis" is a group of methods that constitute the gold standard for an AI dialog system with sentiment detection. These methods can automatically analyze a person's psychological state from their speech, voice color, facial expression, and posture and are crucial for human-centered AI systems. The technique could potentially realize an emotionally intelligent AI with beyond-human capabilities, which understands the user's sentiment and generates a response accordingly. However, current emotion estimation methods focus only on observable information and do not account for the information contained in unobservable signals, such as physiological signals. Such signals are a potential gold mine of emotions that could improve the sentiment estimation performance tremendously.

We are now privy to a spectacular array of communication tools with the potential to connect us all for greater understanding and tolerance. But SA software is counterproductive for open dialog at best, and fundamentally corrosive at worst. It is sure to infuse discord and distrust, much the way the internet is now viewed--isolating us, dividing us into segmented groups--when, at the net's inception, it was supposed to unite the planet. Even more destructive, SA software for SaaS products, like spying on our kids, or when used for marketing to influence elections by insighting ignorant, angry people to elect the second-coming of Hitler, has, and will continue to put even greater distance between us.

Every day a large number of social media users are produced who can be used to analyze their ideas on any event, film, product or politics. Common tools like Apache Storm analyze streams in micro-batch while novel tools like Apache Spark process data in real time to make analyzing and processing real-time data possible.

The function for the negation handler is available at my Github repo. An example of the function output is shown below. 'Negation' is the main function being called on the tokenized sentence as shown. In the function, whenever a negation word (like'not', "n't", 'non-', 'un-', etc) is encountered, a set of cognitive synonyms called synsets are generated for the word next to the negation. These synsets are interlinked by conceptual semantic and lexical relations to each other in a lexical database called WordNet.

This program will give you in-depth knowledge of how NLP and sentiment analysis helps you determine the emotional meaning of communications. This program will give you in-depth knowledge of how NLP and sentiment analysis helps you determine the emotional meaning of communications. You'll learn how NLP applications and Sentiment analysis help you to read, understand, and decode human words in a valuable manner. This program will walk you through different NLP algorithms, and you'll get practical knowledge on how to write code in Python, and implement NLP algorithms. This program will help you learn NLP, Sentiment Analysis, and Deep Learning from basic to advance.