Graduate-level CS programs in the U.S. increasingly enroll international students, with 60.2 percent of master's degrees in 2023 awarded to non-U.S. students. Many of these students take online courses, where peer feedback is used to engage students and improve pedagogy in a scalable manner. Since these courses are conducted in English, many students study in a language other than their first. This paper examines how native versus non-native English speaker status affects three metrics of peer feedback experience in online U.S.-based computing courses. Using the Twitter-roBERTa-based model, we analyze the sentiment of peer reviews written by and to a random sample of 500 students. We then relate sentiment scores and peer feedback ratings to students' language background. Results show that native English speakers rate feedback less favorably, while non-native speakers write more positively but receive less positive sentiment in return. When controlling for sex and age, significant interactions emerge, suggesting that language background plays a modest but complex role in shaping peer feedback experiences.

Abstract--Sentiment analysis plays a crucial role in understanding developer interactions, issue resolutions, and p roject dynamics within software engineering (SE). While traditio nal SE-specific sentiment analysis tools have made significant s trides, they often fail to account for the nuanced and context-depen dent language inherent to the domain. This study systematically evaluates the performance of bidirectional transformers, such as BERT, against generative pre-trained transformers, speci fically GPT -4o-mini, in SE sentiment analysis. Th e results reveal that fine-tuned GPT -4o-mini performs comparab le to BERT and other bidirectional models on structured and balan ced datasets like GitHub and Jira, achieving macro-averaged F1 - scores of 0.93 and 0.98, respectively. However, on linguist ically complex datasets with imbalanced sentiment distributions, such as Stack Overflow, the default GPT -4o-mini model exhibits superior generalization, achieving an accuracy of 85.3% co m-pared to the fine-tuned model's 13.1%. The study underscores the importance of aligning model architectures with dataset characterist ics to optimize performance and proposes directions for future re search in refining sentiment analysis tools tailored to the SE domai n. Sentiment analysis, a critical subfield of natural language processing (NLP), involves classifying text into sentimen t polarities, such as positive, neutral, and negative. It has been widely studied across various domains, including software engineering (SE), where analyzing sentiments expressed in textual artifacts provides insights into developer intera ctions, issue resolution, and project dynamics.

If sentiment analysis tools were valid classifiers, one would expect them to provide comparable results for sentiment classification on different kinds of corpora and for different languages. In line with results of previous studies we show that sentiment analysis tools disagree on the same dataset. Going beyond previous studies we show that the sentiment tool used for sentiment annotation can even be predicted from its outcome, revealing an algorithmic bias of sentiment analysis. Based on Twitter, Wikipedia and different news corpora from the English, German and French languages, our classifiers separate sentiment tools with an averaged F1-score of 0.89 (for the English corpora). We therefore warn against taking sentiment annotations as face value and argue for the need of more and systematic NLP evaluation studies.

Lexicon-based approaches to sentiment analysis of text are based on each word or lexical entry having a pre-defined weight indicating its sentiment polarity. These are usually manually assigned but the accuracy of these when compared against machine leaning based approaches to computing sentiment, are not known. It may be that there are lexical entries whose sentiment values cause a lexicon-based approach to give results which are very different to a machine learning approach. In this paper we compute sentiment for more than 150,000 English language texts drawn from 4 domains using the Hedonometer, a lexicon-based technique and Azure, a contemporary machine-learning based approach which is part of the Azure Cognitive Services family of APIs which is easy to use. We model differences in sentiment scores between approaches for documents in each domain using a regression and analyse the independent variables (Hedonometer lexical entries) as indicators of each word's importance and contribution to the score differences. Our findings are that the importance of a word depends on the domain and there are no standout lexical entries which systematically cause differences in sentiment scores.

Product-related reviews are based on users' experiences that are mostly shared on videos in YouTube. It is the second most popular website globally in 2021. People prefer to watch videos on recently released products prior to purchasing, in order to gather overall feedback and make worthy decisions. These videos are created by vloggers who are enthusiastic about technical materials and feedback is usually placed by experienced users of the product or its brand. Analyzing the sentiment of the user reviews gives useful insights into the product in general. This study is focused on three smartphone reviews, namely, Apple iPhone 13, Google Pixel 6, and Samsung Galaxy S21 which were released in 2021. VADER, which is a lexicon and rule-based sentiment analysis tool was used to classify each comment to its appropriate positive or negative orientation. All three smartphones show a positive sentiment from the users' perspective and iPhone 13 has the highest number of positive reviews. The resulting models have been tested using N\"aive Bayes, Decision Tree, and Support Vector Machine. Among these three classifiers, Support Vector Machine shows higher accuracies and F1-scores.

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The words we use and the tone we inflect paint a picture of the ideas we're expressing. Whether in an online meeting, conducting a remote sales presentation, or hosting a live webinar, the emotions that come through can offer key insights. Video conferencing with Sentiment Analysis provides businesses with the unparalleled opportunity to gain a deeper understanding of what's being said amongst prospects, clients, and employees during online meetings and syncs. Intelligent emotion-reading algorithms pull out the meaning behind the text as a way to explore participant satisfaction and so much more. Here's how using video conferencing and Sentiment Analysis can work together to identify and quantify key emotional indicators and help you get a more detailed understanding of what your audience needs.

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.

Listening to what's being said about your brand can be invaluable for any business. Humans can identify positive and negative sentiments, identify slang, sarcasm, irony, and more. However, the enormous volumes of chatter on the internet make it difficult to determine the overall public sentiments. No need to get anxious, that is exactly what sentiment analysis tools are for. Sentiment analysis tools can help you compile and analyze everything that's being said about your brand.

Matt Canada has a background in graphic design, customer service and management. Sentiment analysis will indicate ways to build a better marketing campaign for your brand. In this article, we will look into four ways to leverage sentiment analysis tools to enhance your brand presence and excite customers. Sentiment analysis is a method to analyze emotions and reactions expressed through online communication - verbal or written. Also termed as'opinion mining' or'emotion AI', sentiment analysis executes data mining, fetches results, and skims out public opinion from within content pieces to help brands get informed of their customer experience.