scholarly journals Chatbots Explain Themselves: Designers' Strategies for Conveying Chatbot Features to Users

2018 ◽  
Vol 9 (3) ◽  
pp. 1
Author(s):  
Francisco Albernaz Machado Valério ◽  
Tatiane Gomes Guimarães ◽  
Raquel Oliveira Prates ◽  
Heloisa Candello
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Social Networks ◽  
Inspection Method ◽  
Communicative Strategies

Recently, text-based chatbots had a rise in popularity, possibly due to new APIs for online social networks and messenger services, and development platforms that help dealing with all the necessary Natural Language Processing. But, as chatbots use natural language as interface, their users may struggle to discover which sentences the chatbots will understand and what they can do. Because of that it is important to support their designers in deciding how to convey the chatbots’ features, as this might determine whether the user will continue chatting or not. In this work, our goal is to analyze the communicative strategies used by popular chatbots when conveying their features to users. We used the Semiotic Inspection Method (SIM) for that end, and as a result we were able to identify a series of strategies used by the analyzed chatbots for conveying their features to users. We then consolidate these findings by analyzing other chatbots. Finally, we discuss the use of these strategies, as well as challenges for designing such interfaces and limitations of using SIM on them.

scholarly journals Uncovering Cybercrimes in Social Media through Natural Language Processing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Julián Ramírez Sánchez ◽  
Alejandra Campo-Archbold ◽  
Andrés Zapata Rozo ◽  
Daniel Díaz-López ◽  
Javier Pastor-Galindo ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Media ◽  
Natural Language Processing ◽  
Law Enforcement ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Law Enforcement Agencies ◽  
Illegal Activities

Among the myriad of applications of natural language processing (NLP), assisting law enforcement agencies (LEA) in detecting and preventing cybercrimes is one of the most recent and promising ones. The promotion of violence or hate by digital means is considered a cybercrime as it leverages the cyberspace to support illegal activities in the real world. The paper at hand proposes a solution that uses neural network (NN) based NLP to monitor suspicious activities in social networks allowing us to identify and prevent related cybercrimes. An LEA can find similar posts grouped in clusters, then determine their level of polarity, and identify a subset of user accounts that promote violent activities to be reviewed extensively as part of an effort to prevent crimes and specifically hostile social manipulation (HSM). Different experiments were also conducted to prove the feasibility of the proposal.

Word Embedding Techniques for Sentiment Analyzers

2021 ◽  
pp. 233-252
Author(s):  
Upendar Rao Rayala ◽  
Karthick Seshadri
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Word Embedding ◽  
Word Embeddings ◽  
The Public ◽  
The Given ◽  
Research Domain

Sentiment analysis is perceived to be a multi-disciplinary research domain composed of machine learning, artificial intelligence, deep learning, image processing, and social networks. Sentiment analysis can be used to determine opinions of the public about products and to find the customers' interest and their feedback through social networks. To perform any natural language processing task, the input text/comments should be represented in a numerical form. Word embeddings represent the given text/sentences/words as a vector that can be employed in performing subsequent natural language processing tasks. In this chapter, the authors discuss different techniques that can improve the performance of sentiment analysis using concepts and techniques like traditional word embeddings, sentiment embeddings, emoticons, lexicons, and neural networks. This chapter also traces the evolution of word embedding techniques with a chronological discussion of the recent research advancements in word embedding techniques.

scholarly journals Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing

2021 ◽  
Vol 30 (01) ◽  
pp. 257-263
Author(s):  
Natalia Grabar ◽  
Cyril Grouin ◽  
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Scientific Literature ◽  
The Past ◽  
Clinical Natural Language Processing ◽  
Selection Of

Summary Objectives: To analyze the content of publications within the medical NLP domain in 2020. Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues. Results: Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included. Conclusion: The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks

Measuring happiness of large-scaled online Turkish unstructured data (Preprint)

2020 ◽  
Author(s):  
Esra Kahya Özyirmidokuz ◽  
Kumru Uyar ◽  
Raian Ali ◽  
Eduard Alexandru Stoica ◽  
Betül Karakaş
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Well Being ◽  
Emotional Awareness ◽  
Large Scale Data ◽  
Processing Algorithms ◽  
Scale Data

BACKGROUND Measuring online Turkish happiness requires a Turkish happiness dictionary which could reflect norms and social values more culturally and linguistically instead of using a translation-oriented method. Analyzing data without neglecting cultural characteristics will not be reliable. Turkish translation of an English word in the Affective Norms of English Words (ANEW) dictionary does not express the same feeling of a Turkish word. In addition, existing emotional dictionaries are not developed for specifically for the social networks with emoticons. OBJECTIVE This research presents the Turkish Happiness Index (THI) which is a set of psychological normative happiness scores to measure an average level of happiness of Turkish online unstructured large-scale data. A well-being informatics analytics research is also done by using THI. METHODS Turkish Happiness Index was completely generated on social networks. 20000 words were extracted with web text mining from social networks. Natural Language Processing algorithms were applied. After data reduction quantitative research methodology is applied. The happiness scores were based detected based on 667 participants’ subjective happiness levels and their thoughts about the 1874 Turkish words. Alexithymia scale was also used to identify the emotional awareness of the participants. The evaluations of the words were done in the dimension of valence using the Self-Assessment Manikin in an online platform. NLP was used to measure online Turkish happiness of data. Data was collected from Facebook with negative #war and positive #family hashtags in a duration of one month using a 3rd party software tool. Natural language processing algorithms including tokenization, transformation, filtering and stemming after converting data to documents. The happiness levels of the documents based on hashtags were determined using the Turkish Happiness Index dictionary. RESULTS THI which contains 345 words and their happiness scores in the Turkish language was developed. The THI is given in Appendix 1. We also put a comparison between words of dictionaries to understand the cultural differences. CONCLUSIONS THI provide researchers with standard materials through which they can automatically measure online happiness of Turkish large-scale data. THI can be used in in real-time big data analytics.

Identifying Suggestions in Airline-User Tweets Using Natural Language Processing and Machine Learning

2021 ◽  
pp. 481-498
Author(s):  
Rafael Jiménez ◽  
Vicente García ◽  
Karla Olmos-Sánchez ◽  
Alan Ponce ◽  
Jorge Rodas-Osollo
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
The Many ◽  
Processing Techniques

Social networks have moved from online sites to interact with your friends to a platform where people, artists, brands, and even presidents interact with crowds of people daily. Airlines are some of the companies that use social networks such as Twitter to communicate with their clients through messages with offers, travel recommendations, videos of collaborations with YouTubers, and surveys. Among the many responses to airline tweets, there are users' suggestions on how to improve their services or processes. These recommendations are essential since the success of many companies is based on offering what the client wants or needs. A database of tweets was created using user tweets sent to airline accounts on Twitter between July 30 (2019) and August 8 (2019). Natural language processing techniques were used on the database to preprocess its data. The latest classification results using Naive Bayes show an accuracy of 72.44%.

scholarly journals Prediction of suicidal ideation in young people from the analysis of texts in social networks written in Mexican spanish: a review of the state of the art

2020 ◽  
pp. 29-33
Author(s):  
Gabriel AGUILERA-GONZÁLEZ ◽  
Christian PADILLA-NAVARRO ◽  
Carlos ZARATE-TREJO ◽  
Georges KHALAF
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Early Detection ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
The State ◽  
World Health ◽  
Mexican Spanish ◽  
The World

Suicide prevention is one of the great issues of the current era. Institutions such as the World Health Organization, have continued to search for all possible alternatives for early detection and timely prevention. Suicide rates have grown more and more in the world, and Mexico, although it is not the country with the most suicides, is one of the countries with the highest growth in recent years. At present, the use of social networks has generated great changes in the way we communicate. Expressing yourself through a social network begins to be more common than expressing ourselves to human beings. Several studies, which will be presented later, show that it is possible to determine from the content of social networks: cases of depression, risk of suicide, and other mental problems. The use of technological tools, such as Natural Language Processing, has served as an effective ally for the early detection of risks, such as abuse, bullying or even detecting emotional problems. The present research seeks to carry out an in-depth analysis in the state of the art of the application of Natural Language Processing as an ally for the detection of suicide risk from the analysis of texts for Mexican Spanish in Social Networks.

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