BigSEM for Text Data
Text data is increasingly recognized as a rich source of information, offering insights that traditional quantitative measures may overlook. Modern natural language processing (NLP) offers a variety of techniques for analyzing text, such as sentiment analysis (Wankhade et al., 2022), topic modeling (Vayansky & Kumar, 2020), and word embedding (Wang et al., 2019). These techniques automatically extract information from text and transform it into meaningful values or vectors, by-passing the need for labor-intensive manual coding.
Structural equation modeling (SEM) is a popular tool in the social and behavioral sciences for analyzing relationships between observed and latent variables. Incorporating textual data into SEM provides a promising avenue for researchers to integrate qualitative and quantitative data analysis. In response to this opportunity, we developed TextSEM, an R package designed to incorporate text data within SEM frameworks. This package leverages advanced NLP techniques to convert text into latent variables, integrate them into SEM model, and conduct estimation.
Here, we demonstrate the practical application of TextSEM through examples using a teaching evaluation dataset.
Example data
For illustration, we use a set of student evaluation of teaching data. The data were scraped from...
Text Sentiment
Sentiment analysis is the process of systematically identifying and quantifying the sentiment exp...
Text Embedding and Encoders
Embedding techniques are widely used in modern NLP. These methods transform text into numerical v...
Use of the R package TextSEM
The R package TextSEM can be used for SEM analysis with text data. To install the package, please...
Use of Web App
One can conduct the analysis by drawing a path diagram. To start, click the "Path Diagram" button...
Video tutorials text data analysis
Here we show how to conduct different types of analysis. Mediation analysis with dictionary-base...