Articles tagged with Sentiment Analysis
Researchers found that hedge funds that effectively bet against public sentiment outperformed those that rode sentiment, earning a 0.4% monthly premium. This result persists even after controlling for fund characteristics and economic risks.
A study published by Michigan Medicine suggests that AI can help increase the chance of people with risky drinking patterns or signs of alcohol use disorder getting outreach and help. The NLP tool analyzed full text of health records and identified over 47,500 patients with some sign of risky drinking.
Bezzo's project enables embodied cognition capabilities in large language models, equipping robots with human-like understanding of human intent. The work has wide-ranging potential for assistive technologies, collaborative robots, and defense missions.
Researchers analyzed over 50,000 barrage comments from a promotional video on Chinese platform Bilibili, finding a clear link between positive emotional comments and purchasing intent. The study also observed real-time emotional contagion and nuanced dynamics in viewer behavior.
Researchers developed a novel approach called R3DG that analyzes representations at varying granularities to capture nuanced emotional fluctuations and reduce computational complexity. This framework demonstrates superior performance in multiple multimodal tasks, including sentiment analysis, emotion recognition, and humor detection.
University of Missouri researchers create digital sentiment map using AI to analyze public Instagram posts, linking emotional tone to real-life features. The tool aims to improve city services, identify areas of concern, and inform emergency response decisions.
Researchers developed an AI-based music creation support system called Amuse, which converts user inputs into harmonic structures to support composition. The system has high potential as a creative companion for musicians and is centered on the creator's initiative.
A new machine learning-based method analyzes corporate reporting for responsibility and innovation, providing insights into company actions. Essi Nousiainen's research also explores blockchain-related trends in corporate reports.
A new analysis of social media posts reveals that public support for solar energy remains high, but has become increasingly polarized. The study found that attitudes toward solar energy are largely being driven by politics, with significant differences in support across regions and states.
Researchers found that up to 34% of the British public considered acts like nepotism or bribery acceptable during the vaccine rollout. The study also discovered an association between public tolerance of petty corruption and vaccine rollout speed, with slower rollouts in areas where tolerance was higher. Lead author Dr Franziska Sohns ...
A recent survey by Purdue University found that nearly 21% of households in heavily affected states reported sometimes or often not having enough to eat within seven days of the storms' impact. Many respondents increased their food spending due to stockpiling, price increases, and access issues.
A study of Reddit comments on ChatGPT's launch reveals the tech community is more strongly divided in their opinions about AI. While non-tech discussions focus on social issues like job replacement, tech-centric subreddits have more focused and opinionated debates.
A Chinese research team introduced a novel two-stage framework using stacked transformers for multimodal sentiment analysis, improving the analysis of emotions expressed through modality combinations. The framework was tested on three open datasets and performed better than or as well as benchmark models.
A recent study analyzing 300,000 X posts found that 48 US states have a more positive than negative tone towards nuclear energy, with a national average at 54% positive. Concerns about waste, cost, and safety dominate negative sentiment, while technology themes fuel positive sentiments highlighting innovations and job creation.
Researchers developed a multimodal algorithm for improved sarcasm detection, examining multiple aspects of audio recordings for increased accuracy. The approach combines sentiment analysis using text and emotion recognition using audio for a comprehensive analysis.
A study published in Economic Inquiry found that U.S. party affiliation significantly impacts consumer sentiment, which in turn affects spending intentions. The analysis shows that consumers' attitudes towards personal and national economic conditions change after elections based on their party affiliation.
Sentiment analysis of tweets from ten countries reveals that happiness levels can recover from big shocks like pandemic lockdowns and Ukraine invasion in as little as 2-3 weeks. The study re-examines adaptation theory using Big Data.
A recent study by Tokyo University of Science has identified central emotions across languages through word association-based colexification networks. The researchers found that concepts like GOOD, WANT, BAD, and LOVE are associated with many other words representing emotions.
Researchers analyzing NIH-funded projects from 1985 to 2020 found a dramatic growth in 'hype' adjectives. Hype terms correlated with funding application trends, suggesting that grant applications influence subsequent publication language. The study highlights the role of funding mechanisms in shaping research communication.
A recent study has uncovered a remarkable connection between individuals' musical preferences and their moral values, shedding light on the influence of music on morality. The research found that specific lyrics and audio features from favorite songs can predict moral values such as Care and Fairness and Loyalty.
Virginia Tech researchers analyzed partisan media sentiment toward AI and found that liberal-leaning media tend to have a more negative tone than conservative media. The study suggests that this opposition can be attributed to concerns over AI amplifying existing social biases, such as racial and income disparities.
Researchers analyzed Twitter posts about the Philadelphia beverage tax before and after its implementation, finding a shift from anger to acceptance. The study's findings highlight the potential of social media data to influence public opinion and provide valuable insights for health policy makers.
A new study reveals a consistent difference in favorability ratings between gene editing and genetically modified organisms (GMOs) in social and traditional media. Gene editing consistently receives higher favorability ratings, with close to 100% achieved in numerous monthly values, indicating a positive shift in public sentiment.
Researchers at the University of Missouri found that adaptive clothing customers face challenges with website usability, limited design and functionality, and sizing issues. The study provides guidelines for retailers to design products that cater to people with disabilities, promoting confidence and workplace wearability.
A new study analyzing YouTube comments found that fans of a celebrity will often continue to support them despite immoral acts. The study used language-processing algorithms to analyze comments on Logan Paul's videos before and after a scandalous incident in which he shared a video of a dead body.
A study by Inga Popovaitė suggests that women may be better suited for long-term space missions due to their positive and supportive leadership style. Women tend to focus on mutual support, motivation, and a positive environment, which can be beneficial in extreme situations where interpersonal conflicts can jeopardize team success.
Researchers at New York University found that positive tweets about vaccines precede increases in vaccination rates, while negative tweets follow decreases. The study uses big data analytics and natural language processing to track vaccine-related topics and sentiment scores.
A study from the University of Illinois Chicago explores key themes and topics from COVID-19 vaccine-related tweets, identifying changes in public attitudes towards vaccination and concerns about effectiveness. The research provides insights for policymakers to frame policies and programs to accelerate vaccine uptake.
A machine learning model has been trained to accurately identify individuals with post-traumatic stress disorder (PTSD) by analyzing text data. The model achieved an 80% accuracy rate in distinguishing between those with and without PTSD. This breakthrough could lead to the development of a cost-effective screening tool for health prof...
Researchers integrated biological signals with gold-standard machine learning methods to create emotionally intelligent speech dialog systems. The study found that combining language information with biological signal information improved the AI's performance, making it comparable to human-like emotional recognition.
A new study by MIT researchers analyzed hundreds of millions of social media posts to measure the emotional toll of the pandemic. The study found that the pandemic caused a huge drop in positive public sentiment, with a subsequent slow return to pre-pandemic levels. Countries most affected included Australia, Spain, and the UK.
Climate change sentiments were overwhelmingly pro-human activity action, while vaccine sentiments showed a significant gap between pro and neutral views. The study analyzed 87 million tweets from 2007 to 2016 and found less interaction among users with opposing views on vaccines compared to climate change.
Researchers develop a methodological framework to extract and monitor information from consumer reviews, providing actionable insights on product attributes and their benefits. The study also extends sentiment analysis by demonstrating hierarchical sentiment analysis, enabling managers to generate tailored dashboards and inform decisions.
Research at RMIT University uses Getty's top lists of editorial pictures to analyze daily investor sentiment, predicting stock market returns based on global mood. The algorithm produces a daily score from 10 popular photos, providing a quick snapshot of investment mood across developed and emerging economies.
Researchers analyzed over 120 million English-language tweets to find a decrease in negative posts about COVID-19, particularly in countries with high vaccination rates. The study suggests that increased vaccination may have contributed to the drop in negativity, but further analysis is needed to fully understand this phenomenon.
A recent study by Bocconi University researchers found that stakeholders' sentiment toward a new CEO has a stronger effect on post-succession performance than the CEO's previous experience and fit. Negative sentiment can undermine a CEO's effectiveness, especially for outside CEOs.
A recent study by AAAS found that Democrats used a slightly negative tone compared to Republicans, who employed more positive language on COVID-19 messaging. The analysis also shows that tone plays a crucial role in engaging the public during a crisis.
A team of researchers developed an AI technique to accurately detect sarcasm in social media text. The model identifies patterns and cue words that indicate sarcasm, enabling more effective customer feedback analysis.
The COVID-19 Health Worker Voices project uses anonymous WhatsApp channel to collect stories and insights from healthcare workers, providing valuable information on challenges, innovations, and coping strategies. Researchers aim to engage frontline workers, give them a voice, and share their experiences to support global health efforts.
Researchers assessed over 4,000 studies on species reintroduction and found that conservation success rates have improved over time. Machine learning and natural language processing techniques helped identify key indicators of positive sentiment, such as words like 'success,' 'protect,' and 'growth.'
The study analyzed Trump's tweets from 2009 to 2018, identifying four style variations: conversational, campaigning, advisory, and engaged. These stylistic patterns suggest underlying communication strategies used by Trump during the 2016 campaign, including appealing to different audiences, defending criticism, and attacking opponents.
A Carnegie Mellon University analysis of #MeToo media coverage finds that accusers are frequently depicted sympathetically yet lack agency, threatening the movement's empowerment objectives. The study reveals a consistent trend in which women are portrayed less powerfully than men.
The study found that the expression of anger in popular music increased gradually over seven decades, peaking in 2015. The expression of sadness, disgust, and fear also increased over time, with joy becoming a milder tone in recent years.
Researchers identified six core emotional storylines: rags to riches, richness to rags, man in a hole, icarus, Cinderella, and Oedipus. These findings may help create compelling stories and teach common sense to AI systems.