Search results
Results from the WOW.Com Content Network
Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. [46] Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored. [ 47 ]
Scientific researchers incorporate text mining approaches into efforts to organize large sets of text data (i.e., addressing the problem of unstructured data), to determine ideas communicated through text (e.g., sentiment analysis in social media [15] [16] [17]) and to support scientific discovery in fields such as the life sciences and ...
Multimodal sentiment analysis is a technology for traditional text-based sentiment analysis, which includes modalities such as audio and visual data. [1] It can be bimodal, which includes different combinations of two modalities, or trimodal, which incorporates three modalities. [ 2 ]
A further step in analysis is the distinction between dictionary-based (quantitative) approaches and qualitative approaches. Dictionary-based approaches set up a list of categories derived from the frequency list of words and control the distribution of words and their respective categories over the texts.
Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
In other words, data analysis is the phase that takes filtered data as input and transforms that into information of value to the analysts. Many different types of analysis can be performed with social media data, including analysis of posts, sentiment, sentiment drivers, geography, demographics, etc. The data analysis step begins once we know ...
The continuous approach tends to use dimensions such as negative vs. positive, calm vs. aroused. The categorical approach tends to use discrete classes such as happy, sad, angry, fearful, surprise, disgust. Different kinds of machine learning regression and classification models can be used for having machines produce continuous or discrete labels.
The probabilistic model of LSA does not match observed data: LSA assumes that words and documents form a joint Gaussian model (ergodic hypothesis), while a Poisson distribution has been observed. Thus, a newer alternative is probabilistic latent semantic analysis, based on a multinomial model, which is reported to give better results than ...