Machine-learning-based deep semantic analysis approach for forecasting new technology convergence

Again, ELM-HL performs the poorest among all classifiers with 76.04 on accuracy. The C5.0 DT classifier model is used which carries out recursive partitioning on extracted datasets to predict job for a specified user’s input. Association rule mining is used to split the feature vector at each node of the tree to form different branches as shown in Fig. A typical feature extraction application of Explicit Semantic Analysis is to identify the most relevant features of a given input and score their relevance. Scoring an ESA model produces data projections in the concept feature space.

Is semantic analysis machine learning?

Semantic analysis is a subfield of NLP and Machine learning that helps in understanding the context of any text and understanding the emotions that might be depicted in the sentence. This helps in extracting important information from achieving human level accuracy from the computers.

For example, there are an infinite number of different ways to arrange words in a sentence. Also, words can have several meanings and contextual information is necessary to correctly interpret sentences. Just take a look at the following newspaper headline “The Pope’s baby steps on gays.” This sentence clearly has two very different interpretations, which is a pretty good example of the challenges in natural language processing. Table 14 compares performance of the proposed model with existing systems when compared on D3 dataset.

Techniques of Semantic Analysis

These two sentences mean the exact same thing and the use of the word is identical. A “stem” is the part of a word that remains after the removal of all affixes. For example, the stem for the word “touched” is “touch.” «Touch» is also the stem of “touching,” and so on. The letters directly above the single words show the parts of speech for each word . For example, “the thief” is a noun phrase, “robbed the apartment” is a verb phrase and when put together the two phrases form a sentence, which is marked one level higher.

srml model

If combined with machine learning, semantic analysis lets you dig deeper into your data by making it possible for machines to pull purpose from an unstructured text at scale and in real time. It is a branch of machine learning that in recent years gave rise to developments that outperformed their predecessors by large margins. Table 16 compares the proposed system with the state-of-the-art models on dataset D5 i.e., SemEval-2017 Task 4B. This dataset is designed for two-point scale classification of the messages with given topics. The proposed CFS augmented BTE system outperforms all other compared models with values of 92.83%, 91.06% and 94.74% accuracy, AvgRec and F1-score respectively. The other top three systems for this task used CNNs and LSTMs with Attention model. From Table 7, using OMD dataset , for positive class it can be seen that BTE outperforms on all parameters with an accuracy of 87.82%.

Examples of Semantic Analysis

Both the feature extraction and classification versions of ESA can be applied to numeric and categorical input data as well. It’s notable for the fact that it contains over 11,000 sentences, which were extracted from movie reviews and accurately parsed into labeled parse trees. This allows recursive models to train on each level in the tree, allowing them to predict the sentiment first for sub-phrases in the sentence and then for the sentence as a whole. Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks. In the larger context, this enables agents to focus on the prioritization of urgent matters and deal with them on an immediate basis. It also shortens response time considerably, which keeps customers satisfied and happy.

What is Natural Processing Language, Applications, and Challenges? – Analytics Insight

What is Natural Processing Language, Applications, and Challenges?.

Posted: Sun, 29 Jan 2023 08:00:00 GMT [source]

However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context. All these parameters play a crucial role in accurate language translation. Semantic technologies such as text analytics, sentiment analysis, and semantic search, empower computers to quickly process text and speech using natural language processing. They automate the process of accurately discovering the correct meaning of words and phrases in text-based computer files. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet.

Machine learning algorithm-based automated semantic analysis

For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. Lexical semantics‘ and refers to fetching the dictionary definition for the words in the text. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. Semantic analysis tech is highly beneficial for the customer service department of any company.

  • Since convolutions occur on adjacent words, the model can pick up on negations or n-grams that carry novel sentiment information.
  • The reduced features are validated on four classifiers namely LOGR-SAG, ANN-GD, SVM-Linear and the Majority Voting Ensemble .
  • This technology is already being used to figure out how people and machines feel and what they mean when they talk.
  • It is a complex system, although little children can learn it pretty quickly.
  • Simply put, semantic analysis is the process of drawing meaning from text.
  • We start off with the meaning of words being vectors but we can also do this with whole phrases and sentences, where the meaning is also represented as vectors.

To determine the sentiment of diverse texts, the authors used POS tags, word n-grams, and tweet context information elements such as hashtags, retweets, emoticons, capital terms, and so on. By evaluating the performance of several classifiers in terms of accuracy, Govindarajan created a hybrid sentiment classification model. For sentiment analysis, a hybrid classifier was created utilizing NB and Genetic Algorithm. Carvalho, Prado & Plastino proposed a statistical method to classify tweets which uses genetic algorithm to determine the pattern words. This algorithm looks over a list of pattern words to find a subset of them that improves classification accuracy considerably.

Cdiscount’s semantic analysis of customer reviews

It is recognized that the semantic space of machine knowledge is a hierarchical concept network , which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis . The semantic hierarchy among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification .


Technologies such as semantic analysis machine learnings, Machine Learning and Text Classification, allow you to conduct a logical analysis of texts, identifying semantic relationships and possible connections between words and extrapolating concepts. Continue reading this blog to learn more about semantic analysis and how it can work with examples. This technology is already being used to figure out how people and machines feel and what they mean when they talk.

Text Exploratory Analysis

After allocating this probability, we use the ensemble technique to provide weight to each classifier depending on its accuracy. Finally, the computer generates the tweet’s positive and negative score based on each classifier’s prediction. Considering heterogeneity of the datasets, the use of conventional feature extraction methods such as information gain etc. might lead to inaccuracy.

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