Difference between revisions of "Sentiment Analysis"
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* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] | * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] | ||
| + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]] | ||
* [http://web.stanford.edu/class/cs124/lec/sentiment2017.pdf Sentiment Analysis | Stanford] | * [http://web.stanford.edu/class/cs124/lec/sentiment2017.pdf Sentiment Analysis | Stanford] | ||
* [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation, Data Labeling, and Auto-Tagging]] | * [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation, Data Labeling, and Auto-Tagging]] | ||
Revision as of 20:52, 2 September 2023
Youtube search... ...Google search
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu
- Sentiment Analysis | Stanford
- Data Augmentation, Data Labeling, and Auto-Tagging
- Sentiment Analysis | Stanford’s Sentiment Analysis Demo using Recursive Neural Networks
- News analysis and filtering
- Watch me Build a Finance Startup | Siraj Raval
Sentiment Analysis algorithms work by referring external resources where the positive and negative polarity of each word is considered. These external words, for which the polarity is predetermined, are known as lexicons. There are several lists of lexicons available and each one focuses on the polarity of a given word in a particular context. A baseline algorithm for SA using SentiWordNet lexicons Packt
Baseline Algorithms
- A Tour of Sentiment Analysis Techniques: Getting a Baseline for Sunny Side Up | Vishal S. - Gab41
- Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The Serbian Movie Review Dataset | V. Batanović, B. Nikolić, and M. Milosavljević
- Sentiment Accuracy: Explaining the Baseline and How to Test It | Paul Barba - Lexalytics
Sentiment Lexicon
- A Survey of Sentiment Lexicons | Sagar Ahire
- SentiWordNet - GitHub a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, objectivity
- Sentiment Treebank | Stanford
- SO-CAL is the Semantic Orientation CALculator, a tool to extract sentiment from text. Sentiment is defined as positive or negative opinion.
- a database of lexical units for a language along with their sentiment orientations. This can be expressed as a set of tuples of the form (lexical unit, sentiment). Here, the lexical units may be words, word senses, phrases, etc.
