In Sentiment analysis, our aim is to detect the emotions as positive, negative, or neutral in a text to denote urgency. We can any of the below two semantic analysis techniques depending on the type of information you would like to obtain from the given data. In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. Now, we have a brief idea of meaning representation that shows how to put together the building blocks of semantic systems. In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation.
Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. Speaking about business analytics, organizations employ various methodologies to accomplish this objective. In that regard, sentiment analysis and semantic analysis are effective tools.
The first improvement involves grouping together aspect synonyms such as “battery” and “battery life” will be grouped in “battery”. The second is to deal with the implicit aspects that are extracted according to the context of the review. Finally, considering the strength of opinion words and proposing a new method to assign more specific sentiment scores are other improvements. Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening.
Moreover, the epidemic has prompted people to switch from offline communication to online or remote communication when dealing with disputes related to products or enterprises (Paska, 2021). Therefore, enterprises have to do so (Obrenovic et al., 2020; Flemming et al., 2021). Semantic analysis is a mechanism that allows machines to understand a sequence of words in the same way that humans understand it. This depends on understanding what the words actually mean and what they refer to based on the context and domain which can sometimes be ambiguous. By knowing the structure of sentences, we can start trying to understand the meaning of sentences. 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.
The experiments are based on the SentiWordnet lexicon, which achieved better performance than the subjectivity lexicon. Whereas, the SentiWordnet lexicon targets all the words, regardless their part of speech tags, unlike the subjectivity lexicon, that targets adjectives only. To give more accurate sentiment scores, the SentiWordnet is used with two lists of most known negative and positive sentiments. The first experiment used the frequently mentioned words in the dataset as product aspects. However, SALOM achieved low precision, recall, and f-measure because of spam words that are considered as product aspects. Therefore, in the second experiment, the semantic similarity is used to find the exact aspects related to the product domain, and the results are improved.
Systematic mapping studies follow an well-defined protocol as in any systematic review. The main differences between a traditional systematic review and a systematic mapping are their breadth and depth. While a systematic review deeply analyzes a low number of primary studies, in a systematic mapping a wider number of studies are analyzed, but less detailed. Thus, the search terms of a systematic mapping are broader and the results are usually presented through graphs.
In this study, we identified the languages that were mentioned in paper abstracts. We must note that English can be seen as a standard language in scientific publications; thus, papers whose results were tested only in English datasets may not mention the language, as examples, we can cite [51–56]. Besides, we can find some studies that do not use any linguistic resource and thus are language independent, as in [57–61]. These facts can justify that English was mentioned in only 45.0% of the considered studies.
Meanwhile, our method gains improvement of 0.58%∼1.06% on other binary sentiment datasets. This implies that our proposed method performs better in dealing with multiclass emotion analysis, the reason is that our method introduces affective semantic concepts as intermediate representations. These excavated semantic concepts are more emotional discriminative and have wider coverage, thus, they benefit more for multi-class emotion datasets. In summary, applying the affective semantic concepts metadialog.com that conform to emotion properties as intermediate representations of images, our method shows significant advantages over the semantic-based image understanding approaches. To evaluate the performance of our method for image emotion classification, we conduct comparison experiments to compare our method with the above-mentioned baselines on five public affective datasets. Table 4 reports the performances of the baselines along with our approach measured by the accuracy metric.
FN (False-Negative) identifies the number of words that are incorrectly classified as not product aspects. TN (True-Negative) identifies the number of words that are correctly classified as not product aspects. All these mentioned reasons can impact on the efficiency and effectiveness of subjective and objective classification. Accordingly, two bootstrapping methods were designed to learning linguistic patterns from unannotated text data. Both methods are starting with a handful of seed words and unannotated textual data.
Semantics is the literal meaning of words and phrases, while pragmatics identifies the meaning of words and phrases based on how language is used to communicate.
Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products’ comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects’ synonyms, hyponyms, and hypernyms to improve the accuracy of classification.
To evaluate the framework, a prototype is developed and applied to two different domains (e-commerce and politics) and the resulting insight knowledge bases constructed. The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41–46]. The high interest in getting some knowledge from web texts can be justified by the large amount and diversity of text available and by the difficulty found in manual analysis.
The algorithm is chosen based on the data available and the type of pattern that is expected. If this knowledge meets the process objectives, it can be put available to the users, starting the final step of the process, the knowledge usage. Otherwise, another cycle must be performed, making changes in the data preparation activities and/or in pattern extraction parameters. If any changes in the stated objectives or selected text collection must be made, the text mining process should be restarted at the problem identification step. Data science involves using statistical and computational methods to analyze large datasets and extract insights from them.
In many social networking services or e-commerce websites, users can provide text review, comment or feedback to the items. These user-generated text provide a rich source of user’s sentiment opinions about numerous products and items. For different items with common features, a user may give different sentiments. Also, a feature of the same item may receive different sentiments from different users. Users’ sentiments on the features can be regarded as a multi-dimensional rating score, reflecting their preference on the items. Automated semantic analysis works with the help of machine learning algorithms.
As shown in Table 6, the proposed model achieves better performance compared to other methods in terms of Recall, Precision and F-measure respectively. As, the proposed SALOM model uses the semantic similarity to avoid spam product aspects. In addition, it uses different types of product aspect, such as aspect synonym, hyponym, and hypernym which also related to the product domain. Moreover, SALOM handles the negation words and differentiates between the negation word “not” and “not only”.
For Example, you could analyze the keywords in a bunch of tweets that have been categorized as “negative” and detect which words or topics are mentioned most often. With the help of meaning representation, we can link linguistic elements to non-linguistic elements. Both polysemy and homonymy words have the same syntax or spelling but the main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. In other words, we can say that polysemy has the same spelling but different and related meanings. Usually, relationships involve two or more entities such as names of people, places, company names, etc. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.
The most important task of semantic analysis is to get the proper meaning of the sentence. For example, analyze the sentence “Ram is great.” In this sentence, the speaker is talking either about Lord Ram or about a person whose name is Ram.
In addition, we also assess the performance of our method on several positive and negative classes’ datasets for binary classification, including Flickr, Instagram, and Twitter datasets. They contain 60745, 42856, and 603 web images from Flickr, Instagram, and Twitter social networks. However, after the application of semantic similarity and the use of real aspects of the product (FE − A) the results are improved. Afterwards, the aspect set is extended by another aspect types (A − ASyns) and (A − ASyns − ARel) to achieve higher performance. As shown in Table 4, some results are not changed after adding a specific aspect type. For example, in mobile phone dataset, results from aspect types (FE − A) and (A − ASyns) using the SentiWordnet lexicon are identical.
Semantic analysis is key to contextualization that helps disambiguate language data so text-based NLP applications can be more accurate. Let's dive deeper into why disambiguation is crucial to NLP. Machines lack a reference system to understand the meaning of words, sentences and documents.