Academic literature on the topic 'Toxic comments classifier'

In healthcare operations, narrative text and comments from questionnaires are common and abundant. Making sense of and coming to some shared meanings around text comments from such questionnaires is often time consuming. A lack of resources and expertise may contribute to hesitation and indecisions when deciding on how or if to analyze text. Because of challenges with analyzing text in operational settings, there can be reluctance to capture rich narrative information. Nonetheless, narrative comments can be a source of rich information that with reliable and faster approaches for analyzing may help with informing operational decisions and human-centered design efforts. In this paper, we describe using text analytics approaches for contributing to thematic analysis of users’ comments to help with health information systems learning.Several text analytic approaches were explored as possible pathways to reduce the burden of reviewing comments about training around health information systems. Approaches included topic modelling, keyword extraction and creating word clouds, word co-occurrence and uniquely co-occurring word visualizations, and text classifiers and nomograms that highlights top linguistic features for the trained classifier. The team walked through example approaches and visualizations and decided on next steps.Visualizations of word co-occurrence and uniquely co-occurring word networks and top linguistic features used to train a naïve-bayes text classifier were used to envision possible categories or codes. Regular expressions were iteratively formulated consisting of some combination of words and stems as codes were formulated and extracts were repeatedly reviewed. Code formulation corresponded with refinement of regular expressions. Individual comments could be multi-labeled and not all comments were coded. Static visuals, text examples, regular expressions, and extract quantities were collected, presented, discussed, and refined with the review team.The purpose of this work was to explore text analytic approaches to assist with response interpretation and to apply filtering techniques for addressing concerns of information overload. Addressing concerns about information overload may reduce hesitation with collecting and examining text. By reframing this as a filtering problem, we began to inquire into ways to review, create codes, and code comments more quickly. Including and fine-tuning text analytics approaches may help teams learn more quickly from questionnaire comments about how users perceive working within health information systems. Finally, lowering thresholds for analyzing text may boost motivations for gathering rich information keeping us from missing out on vital viewpoints and language use across time.

Approximately 80 % of people with some form of physical, mental,or intellectual disability live in developing countries. These samecountries have shown significant growth in the availability of theinternet. Such facts reveal good possibilities regarding access toemotional support and experiences exchange among people withdisabilities through social media. However, hate speech and derogatorycomments about these people can be a recurring problem onthese platforms. In order to identify these posts, this article featuresa classifier developed using Twitter posts related to disabilities.The results show that the tool developed is promising in detectingoffensive and pejorative comments on this topic, which can be usedin content management systems.

Background: In an era of explosive growth in biomedical evidence, improving systematic review (SR) search processes is increasingly critical. Text-mining tools (TMTs) are a potentially powerful resource to improve and streamline search strategy development. Two types of TMTs are especially of interest to searchers: word frequency (useful for identifying most used keyword terms, e.g., PubReminer) and clustering (visualizing common themes, e.g., Carrot2). Objectives: The objectives of this study were to compare the benefits and trade-offs of searches with and without the use of TMTs for evidence synthesis products in real world settings. Specific questions included: (1) Do TMTs decrease the time spent developing search strategies? (2) How do TMTs affect the sensitivity and yield of searches? (3) Do TMTs identify groups of records that can be safely excluded in the search evaluation step? (4) Does the complexity of a systematic review topic affect TMT performance? In addition to quantitative data, we collected librarians' comments on their experiences using TMTs to explore when and how these new tools may be useful in systematic review search¬¬ creation. Methods: In this prospective comparative study, we included seven SR projects, and classified them into simple or complex topics. The project librarian used conventional “usual practice” (UP) methods to create the MEDLINE search strategy, while a paired TMT librarian simultaneously and independently created a search strategy using a variety of TMTs. TMT librarians could choose one or more freely available TMTs per category from a pre-selected list in each of three categories: (1) keyword/phrase tools: AntConc, PubReMiner; (2) subject term tools: MeSH on Demand, PubReMiner, Yale MeSH Analyzer; and (3) strategy evaluation tools: Carrot2, VOSviewer. We collected results from both MEDLINE searches (with and without TMTs), coded every citation’s origin (UP or TMT respectively), deduplicated them, and then sent the citation library to the review team for screening. When the draft report was submitted, we used the final list of included citations to calculate the sensitivity, precision, and number-needed-to-read for each search (with and without TMTs). Separately, we tracked the time spent on various aspects of search creation by each librarian. Simple and complex topics were analyzed separately to provide insight into whether TMTs could be more useful for one type of topic or another. Results: Across all reviews, UP searches seemed to perform better than TMT, but because of the small sample size, none of these differences was statistically significant. UP searches were slightly more sensitive (92% [95% confidence intervals (CI) 85–99%]) than TMT searches (84.9% [95% CI 74.4–95.4%]). The mean number-needed-to-read was 83 (SD 34) for UP and 90 (SD 68) for TMT. Keyword and subject term development using TMTs generally took less time than those developed using UP alone. The average total time was 12 hours (SD 8) to create a complete search strategy by UP librarians, and 5 hours (SD 2) for the TMT librarians. TMTs neither affected search evaluation time nor improved identification of exclusion concepts (irrelevant records) that can be safely removed from the search set. Conclusion: Across all reviews but one, TMT searches were less sensitive than UP searches. For simple SR topics (i.e., single indication–single drug), TMT searches were slightly less sensitive, but reduced time spent in search design. For complex SR topics (e.g., multicomponent interventions), TMT searches were less sensitive than UP searches; nevertheless, in complex reviews, they identified unique eligible citations not found by the UP searches. TMT searches also reduced time spent in search strategy development. For all evidence synthesis types, TMT searches may be more efficient in reviews where comprehensiveness is not paramount, or as an adjunct to UP for evidence syntheses, because they can identify unique includable citations. If TMTs were easier to learn and use, their utility would be increased.