Semantic Text Analysis: On the Structure of Linguistic Ambiguity in Or

These researchers adapted the existing Memory Neural Network model to create a Semantic Memory Neural Network for use in semantic text analysis. They evaluated their new model on different configurations, exploring the breadth of text analysis. The researchers applied different Long Short Term Memory model configurations to their SeMemNN, including configurations double-layer LSTM, one-layer bi-directional LSTM, one-layer bi-directional LSTM with self-attention. They found that their novel model outperformed VDCNN, an existing neural network option. We chose this article for its description of how methods of text analysis evolve.

In recent years, network science methods have arisen in the field of semantic text analysis as ways to improve the speed and accuracy of the analysis. Researchers find network science helpful to categorize and analyze text data when the data inputted is complex, unprocessed, or does not follow clear categorization rules. In our work, we focused on semantic text analysis using a network science approach. The algorithm that we explored took a data set of strings, then transformed it into a network where each node was one of the text fragments from the data set. In the network, two nodes were adjacent if they were considered similar based on criteria meant to evaluate the sentiment of the nodes. We expected that the communities in the resulting network would represent different sentiments.

Latent semantic analysis for text-based research

Since hamming distance counts the differences, two vectorized strings that are identical will have a hamming distance of 0. Therefore, there were no texts that had a hamming value less than the cutoff. This posed a serious issue in creating the network, since we didn’t want to pick an arbitrary cutoff, but we also couldn’t use our version of Foxworthy’s implementation. We eventually scatter-plotted the hamming distances from the kernel matrix, and selected cutoffs based on the distribution. Running some examples, we thought it was more intuitive to change our hamming distance function to track hamming similarity, and count the number of indices that vectors were similar. This way, we could choose cutoffs that were higher on the scatter-plot and further the intuitive sense that a high hamming value means high similarity.

We included this paper because their network analysis was very similar to the other text analysis papers we read, but focused more on the model, and less on the idea of semantic text analysis. We were interested in their expansion of analysis methods to be more versatile to different data sets. This paper proposed an expansion of the text clustering analysis method used in network semantic text analysis, using co-clustering. Clustering text can lead to clusters where the mean value converges toward the cluster center, which is rarely seen in real text data.

What’s The Issue With Sentiment Analysis?

Eventually, companies can win the faith and confidence of their target customers with this information. Sentiment analysis and semantic analysis are popular terms used in similar contexts, but are these terms similar? The paragraphs below will discuss this in detail, outlining several critical points. We also found some studies that use SentiWordNet , which is a lexical resource for sentiment analysis and opinion mining .

Kitchenham and Charters present a very useful guideline for planning and conducting systematic literature reviews. As systematic reviews follow a formal, well-defined, and documented protocol, they tend to be less biased and more reproducible than a regular literature review. Beyond the potential effects of biases, one large limitation of our work was that the method was designed for very short strings, and would have too large a run-time with larger texts. However, we would also consider this to be a strength, since strong network science methods already exist to analyze large texts, and our method focused on a less explored field of shorter texts. We could also imagine that our similarity function may have missed some very similar texts in cases of misspellings of the same words or phonetic matches.

Named Entity Extraction

To vectorize the data set, we combined our earlier functions to preprocess our data set, to compare each string to the feature space, and to create a vector based on the k-grams it contained. This allowed us to test our hamming distance function, which matched Foxworthy’s work. However, at this point we had concerns about runtime, since our data set was very large and we were beginning to work on large matrix and network manipulations in the method.

Therefore, we overall met our research goal of categorizing the data set by sentiment in a time-efficient way, but we could work towards a clearer and more objective categorization methods. The second most used source is Wikipedia , which covers a wide range of subjects and has the advantage of presenting the same concept in different languages. Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. Medelyan et al. present the value of Wikipedia and discuss how the community of researchers are making use of it in natural language processing tasks , information retrieval, information extraction, and ontology building. When looking at the external knowledge sources used in semantics-concerned text mining studies (Fig. 7), WordNet is the most used source.

Sentiment Analysis vs. Semantic Analysis: What Creates More Value?

Besides, WordNet can support the computation of semantic similarity and the evaluation of the discovered knowledge . This mapping shows that there is a lack of studies considering languages other than English or Chinese. The low number of studies considering other languages suggests that there is a need for construction or expansion of language-specific resources (as discussed in “External knowledge sources” section). These resources can be used for enrichment of texts and for the development of language specific methods, based on natural language processing. Grobelnik also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness. The most simple level is the lexical level, which includes the common bag-of-words and n-grams representations.

It is extensively applied in medicine, as part of the evidence-based medicine . This type of literature review is not as disseminated in the computer science field as it is in the medicine and health care fields1, although computer science researches can also take advantage of this type of review. We can find important reports on the use of systematic reviews specially in the software engineering community .

The importance of semantic analysis in NLP

A semi-automatic ontology construction method from text corpora in the domain of radiological protection that is composed of revelation of the significant linguistic structures and forming the templates. It is generally acknowledged that the ability to work with text on a semantic basis is essential to modern information retrieval systems. As a result, the use of LSI has significantly expanded in recent years as earlier challenges in scalability and performance have been overcome. 1999 – First implementation of LSI technology for intelligence community for analyzing unstructured text .

• However, with the implementation of modern high-speed processors and the availability of inexpensive memory, these considerations have been largely overcome.
• Mirza, “Document level semantic comprehension of noisy text streams via convolutional neural networks,” The Institute of Electrical and Electronics Engineers, Inc, pp. 475–479, 2017.
• In this post, we’ll cover the basics of natural language processing, dive into some of its techniques and also learn how NLP has benefited from recent advances in deep learning.
• As a result, they were able to quantify the balance between traditional topics and innovative topics in service industry research, which could be useful to future researchers.
• The coverage of Scopus publications are balanced between Health Sciences (32% of total Scopus publication) and Physical Sciences (29% of total Scopus publication).
• Unlike Gorrell and Webb’s stochastic approximation, Brand’s algorithm provides an exact solution.

The concept-based semantic exploitation is normally based on external knowledge sources (as discussed in the “External knowledge sources” section) [74, 124–128]. As an example, explicit semantic analysis rely on Wikipedia to represent the documents by a concept vector. In a similar way, Spanakis et al. improved hierarchical clustering quality by using a text representation based on concepts and other Wikipedia features, such as links and categories. This paper reports a systematic mapping study conducted to get a general overview of how text semantics is being treated in text mining studies. It fills a literature review gap in this broad research field through a well-defined review process. As a systematic mapping, our study follows the principles of a systematic mapping/review.

• The authors present a chronological analysis from 1999 to 2009 of directed probabilistic topic models, such as probabilistic latent semantic analysis, latent Dirichlet allocation, and their extensions.
• Semantic annotation or tagging is the process of attaching to a text document or other unstructured content, metadata about concepts (e.g., people, places, organizations, products or topics) relevant to it.
• Paper presented at the Third Annual Conference of the Society for Text and Discourse, Boulder, CO.
• After the selection phase, 1693 studies were accepted for the information extraction phase.
• A comparison among semantic aspects of different languages and their impact on the results of text mining techniques would also be interesting.
• However, text mining is a wide research field and there is a lack of secondary studies that summarize and integrate the different approaches.