AI Sentiment

Extracted Attributes

• Nature

  A field of artificial intelligence and natural language processing that identifies, extracts, quantifies, and studies affective states and subjective information from text.

• Associated Risks

  Technology lock-in and a lack of hardware diversity in AI.

• Core Economic Issue

  Perceived mismatch between significant investment in GPU infrastructure (e.g., Nvidia H100 chips) and currently limited Return on Investment (ROI) for AI technologies.

• Current Market Perception

  Bearish (as of mid-2024)

• Applications (Broader Field)

  Analyzing customer feedback, product development, social media monitoring, healthcare, marketing, customer service.

• Key Technologies (Broader Field)

  Natural Language Processing (NLP), Text Analysis, Computational Linguistics, Biometrics, Machine Learning, Deep Language Models.

• Primary Concern Driving Bearish Sentiment

  Massive and potentially unsustainable AI Capital Expenditure (Capex) cycle, particularly in GPU infrastructure.

Timeline

• The All-In Podcast episode 186 is released, featuring a discussion on the shifting AI sentiment which has turned bearish due to concerns over AI capital expenditure, limited ROI, and technology lock-in, while also presenting contrasting bullish long-term views. (Source: web_search_results, related_documents)

  2024-07-12

Sentiment analysis

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.

Web Search Results

• What Is AI Sentiment Analysis and How You Can Use It?

  AI sentiment analysis refers to the use of artificial intelligence technologies, especially natural language processing (NLP), to identify, extract, quantify, and study affective states and subjective information from text. The primary goal of sentiment analysis is to determine the attitude, emotions, or opinions expressed in a given piece of text, which can range from positive, and negative, to neutral, or even more complex emotions like happiness, sadness, anger, etc. [...] In reality, AI sentiment analysis is the key to unlocking customer feedback’s full potential. By focusing on identifying online sentiments, businesses gain profound insights into customer experiences. AI sentiment analysis models go through reviews and big data analytics to understand the emotional undertones. Brands get to adapt, refine, and tailor their offerings based on authentic customer sentiments. [...] ## Table of Contents What is AI-driven Sentiment Analysis? Practical Applications of AI Sentiment Analysis Best 3 AI Sentiment Analysis Tools Why You Should Use an AI Sentiment Analysis Tool? Tips on Using AI Sentiment Analysis Try AI Sentiment Analysis for Free ## What is AI-driven Sentiment Analysis? AI sentiment analysis is a process used in artificial intelligence and natural language processing to determine the emotional tone behind a body of text.

• What is AI sentiment Analysis? Benefits and Use-cases - Pareto.AI

  Final Thoughts AI sentiment analysis is a powerful tool that transforms how businesses understand public opinions and emotions. It has broad applications, from product development to customer service, but also presents challenges like interpreting context and mitigating data biases. [...] Enhanced Customer Insights: AI sentiment analysis decodes the emotions and opinions expressed in customer feedback across various platforms, such as social media, reviews, and surveys. This deep understanding of customer sentiment aids businesses in gauging overall satisfaction, identifying loyal customers, and recognizing emerging trends and preferences, enabling more customer-centric decision-making. [...] Improved Product Development: By systematically analyzing customer feedback, AI sentiment analysis identifies specific product features that are appreciated or criticized. This insight allows companies to tailor their products more closely to consumer needs, leading to more successful products and innovations that are aligned with customer desires.

• The Impact of AI Sentiment Analysis: Benefits and Use Cases

  AI-based sentiment analysis leverages both syntactic and semantic techniques to understand and interpret the sentiment expressed in textual data. Sentiment analysis, a branch of AI, utilizes natural language processing (NLP) and machine learning to automatically classify text into positive, negative, or neutral categories based on embedded emotions and opinions. [...] An important aspect of AI-based sentiment analysis is its capacity to tag large datasets in real-time. As machine learning algorithms evolve, sentiment analysis models enhance their proficiency over time, categorizing new data more accurately. [...] Before we go into the purpose of sentiment analysis in artificial intelligence, let’s first understand what sentiment analysis is and why it matters in digital world. Sentiment analysis, also referred to as opinion mining, it involves the utilization of natural language processing (NLP), text analysis, and computational linguistics to discern and extract subjective content from textual data. This subjective information typically includes a customer or group of customer sentiments opinions,

• A complete guide to Sentiment Analysis approaches with AI - Thematic

  Companies use AI-based solutions to apply aspect-based sentiment analysis across their social media, review sites, online communities and internal customer communication channels. The results of the analysis can then be explored in data visualizations to identify areas for improvement. These visualizations could include overall sentiment, sentiment over time, and sentiment by rating for a particular dataset. [...] Sentiment analysis is automated using Machine Learning. This means that businesses can get insights in real-time. This can be very helpful when identifying issues that need to be addressed right away. For example, a negative story trending on social media can be picked up in real-time and dealt with quickly. If one customer complains about an account issue, others might have the same problem. By instantly alerting the right teams to fix this issue, companies can prevent bad experiences from [...] The challenge here is that machines often struggle with subjectivity. Let’s take the example of a product review which says “the software works great, but no way that justifies the massive price-tag”. In this case the first half of the sentence is positive. But it’s negated by the second half which says it’s too expensive. The overall sentiment of the sentence is negative.

• What Is Sentiment Analysis? - IBM

  The latest artificial intelligence (AI) sentiment analysis tools help companies filter reviews and net promoter scores (NPS) for personal bias and get more objective opinions about their brand, products and services. For example, if a customer expresses a negative opinion along with a positive opinion in a review, a human assessing the review might label it negative before reaching the positive words. AI-enhanced sentiment classification helps sort and classify text in an objective manner, so