A software agent for social networks using natural language processing techniques

2016 ◽  
Author(s):  
Drazen Draskovic ◽  
Vidor Gencel ◽  
Slavko Zitnik ◽  
Marko Bajec ◽  
Bosko Nikolic
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Software Agent ◽  
Processing Techniques

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 A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

Identifying and intercepting health misinformation on Reddit dermatology forums with artificially intelligent bots using natural language processing (Preprint)

2021 ◽  
Author(s):  
Monique B. Sager ◽  
Aditya M. Kashyap ◽  
Mila Tamminga ◽  
Sadhana Ravoori ◽  
Christopher Callison-Burch ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Test Accuracy ◽  
Limited Data ◽  
Test Environment ◽  
Data Set ◽  
Inappropriate Care ◽  
Processing Techniques

BACKGROUND Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to inappropriate care. Initial testing has shown that artificially intelligent bots can detect misinformation on Reddit forums and may be able to produce responses to posts containing misinformation. OBJECTIVE To analyze the ability of bots to find and respond to health misinformation on Reddit’s dermatology forums in a controlled test environment. METHODS Using natural language processing techniques, we trained bots to target misinformation using relevant keywords and to post pre-fabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. RESULTS Our models yielded data test accuracies ranging from 95%-100%, with a BERT fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective pre-fabricated responses to misinformation. CONCLUSIONS Using a limited data set, bots had near-perfect ability to detect these examples of health misinformation within Reddit dermatology forums. Given that these bots can then post pre-fabricated responses, this technique may allow for interception of misinformation. Providing correct information, even instantly, however, does not mean users will be receptive or find such interventions persuasive. Further work should investigate this strategy’s effectiveness to inform future deployment of bots as a technique in combating health misinformation. CLINICALTRIAL N/A

scholarly journals Recent Advances in Conversational Intelligent Tutoring Systems

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 42-54 ◽  
Author(s):  
Vasile Rus ◽  
Sidney D’Mello ◽  
Xiangen Hu ◽  
Arthur Graesser
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Individual Student ◽  
Learning Progressions ◽  
Tutoring Systems ◽  
Recent Advances ◽  
Processing Techniques

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

Applying Natural Language Processing Techniques to Generate Open Data Web APIs Documentation

2020 ◽  
pp. 416-432
Author(s):  
César González-Mora ◽  
Cristina Barros ◽  
Irene Garrigós ◽  
Jose Zubcoff ◽  
Elena Lloret ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Open Data ◽  
Processing Techniques
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