Sentiment Analysis: How To Gauge Customer Sentiment 2024

Thus, this study aims to use natural language processing (NLP) approaches to propose a machine learning framework for text mining of sexual harassment content in literary texts. The proposed framework involves the classification of physical and non-physical types of sexual harassment using a machine-learning model. Lexicon-based sentiment and emotion detection are applied to sentences containing instances of sexual harassment for data labelling and analysis.

OSNs include a huge amount of UGC with many irrelevant and noisy data, such as non-meaningful, inappropriate data and symbols that need to be filtered before applying any text analysis techniques. This is quite difficult to achieve since the objective is to analyze unstructured and semi-structured text data. Without a doubt, employing methods that are similar to human–human interaction is more convenient, where users can specify their preferences over an extended dialogue. Also, there is a need for further effective methods and tools that can aid in detecting and analyzing online social media content, particularly for those using online UGC as a source of data in their systems. We implemented the Gensim toolkit due to its ease of use and because it gives more accurate results. Gensim was the most popular tool used in many recent studies, and it offers more functionality; it also contains an NLP package that has effective implementations of several well-known functionalities for the TM methods such as TF-IDF, LDA, and LSA.

Applications of a sentiment analysis tool

Detecting mental illness from text can be cast as a text classification or sentiment analysis task, where we can leverage NLP techniques to automatically identify early indicators of mental illness to support early detection, prevention and treatment. Sentiment analysis, which involves categorizing sentiments as positive or negative, has been explored across various domains in local contexts. Various researchers have applied machine learning techniques to perform sentiment analysis in domains such as entertainment6, aspect-level sentiment classification from social media7, and deep learning-based Amharic sentiment classification8.

As a result, obtaining fewer keywords can help define the topic in less time, which is useful for our future developing real-time social recommendation system which aims to analyze the user’s online conversation and deliver a suitable task such as advertainment. Based on our experiments, we decided to focus on LDA and NMF topic methods as an approach to analyze short social textual data. Also, Gensim includes several kinds of algorithms such as LDA, RP, LSA, TF-IDF, hierarchical Dirichlet processes (HDPs), LSI, and singular value decomposition (SVD).

Danmaku emotion annotation based on Maslow’s hierarchy of needs theory

The continuous evolution of language, especially with the advent of internet slang and new lexicons in online communication, calls for adaptive models that can learn and evolve with language use over time. These challenges necessitate ongoing research and development of more sophisticated ABSA models that can navigate the intricacies of sentiment analysis with greater accuracy and contextual sensitivity. Also, when comparing LDA and NMF methods based on their runtime, LDA was slower, and it would be a better choice to apply NMF specifically in a real-time system.

Natural language processing (NLP) is a subset of AI which finds growing importance due to the increasing amount of unstructured language data. The rapid growth of social media and digital data creates significant challenges in analyzing vast user data to generate insights. Further, interactive automation systems such as chatbots are unable to fully replace humans due to their lack of understanding of semantics and context.

• It is a Stanford-developed unsupervised learning system for producing word embedding from a corpus’s global phrase co-occurrence matrix.
• Mao et al. (2011) used a wide range of news data and sentiment tracking measures to predict financial market values.
• Researchers, including Mnih and Hinton (2009), explored probabilistic models for learning distributed representations of words.

Sociality can vary across different dimensions, such as social interaction, social patterns, and social activities within different data ages. Consequently, there are no “general rules” or a universally applicable framework for analysing societies or defining a “general world” (Lindgren, 2020). In this context, text mining emerges as an invaluable tool for efficiently analysing large volumes of data.

RNNs are a type of artificial neural network that excels in handling sequential or temporal data. In the case of text data, RNNs convert the text into a sequence, enabling them to capture the relationship between words and the structure of the text. The output of an RNN is dependent on its previous element, allowing it to consider context. LSTM, a widely used architecture for RNNs, is capable of capturing long-term dependencies and influencing current predictions. Additionally, GRU serves as an RNN layer that addresses the issue of short-term memory while utilizing fewer memory resources.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Furthermore, the integration of external syntactic knowledge into these models has shown to add another layer of understanding, enhancing the models’ performance and leading to a more sophisticated sentiment analysis process. Natural language processing (NLP) is a field that combines the power of computational linguistics, computer science, and artificial intelligence to enable machines to understand, analyze, and generate the meaning of natural human speech. The first actual example of the use of NLP techniques ChatGPT was in the 1950s in a translation from Russian to English that contained numerous literal transaction misunderstandings (Hutchins, 2004). Essentially, keyword extraction is the most fundamental task in several fields, such as information retrieval, text mining, and NLP applications, namely, topic detection and tracking (Kamalrudin et al., 2010). In this paper, we focused on the topic modeling (TM) task, which was described by Miriam (2012) as a method to find groups of words (topics) in a corpus of text.

Calculating the semantic sentiment of the reviews

However, the impact of the COVID-19 crisis on the language used in financial newspapers remains underexplored. The present study addresses this gap by comparing data from specialized financial newspapers in English and Spanish, focusing on the years immediately prior to the COVID-19 semantic analysis of text crisis (2018–2019) and during the pandemic itself (2020–2021). We aim to explore how the economic upheaval of the latter period was conveyed in these publications and investigate the changes in sentiment and emotion in their language compared to the previous timeframe.

Therefore, the difference in semantic subsumption between CT and CO does exist in the distribution of semantic depth. On the one hand, U test results indicate a generally higher level of explicitation in verbs of CO than those of CT. On the other hand, the comparison of the distributions reveals that semantic subsumption features of CT are more centralized than those of CO, which can be understood as a piece of evidence for levelling out. The other major effect lies in the conversion and addition of certain semantic roles for logical explicitation. In Structure 3 (Fig. 2), the Chinese translation converted the role of adverbial (ADV) in the source text into a purpose or reason (PRP) by adding the specific logical symbol “由于(because of)”. These instances of conversion and addition are essentially a shift from logical grammatical metaphors to congruent forms that occurs during the translation process, through which the logical semantic is made explicit (Martin, 1992).

Key Features of Sentiment Analysis Tools

I will try fitting a model with three different data; oversampled, downsampled, original, to see how different sampling techniques affect the learning of a classifier. Twitter is a popular social networking service with over 300 million active users monthly, in which users can post their tweets (the posts on Twitter) or retweet others’ posts. Researchers can collect tweets using available Twitter application programming interfaces (API). For example, Sinha et al. created a manually annotated dataset to identify suicidal ideation in Twitter21. Hu et al. used a rule-based approach to label users’ depression status from the Twitter22. However, normally Twitter does not allow the texts of downloaded tweets to be publicly shared, only the tweet identifiers—some/many of which may then disappear over time, so many datasets of actual tweets are not made publicly available23.

Popular neural models used for learning word embedding are Continuous Bag-Of-Words (CBOW)32, Skip-Gram32, and GloVe33 embedding. Skip-Gram follows a reversed strategy as it predicts the context words based on the centre word. GloVe uses the vocabulary words co-occurrence matrix as input to the learning algorithm where each matrix cell holds the number of times by which two words occur in the same context. A discriminant feature of word embedding is that they capture semantic and syntactic connections among words. Embedding vectors of semantically similar or syntactically similar words are close vectors with high similarity29.

These models not only deliver superior performance but also offer better interpretability, making them invaluable for applications requiring clear rationale. The adoption of syntax in ABSA underscores the progression toward more human-like language processing in artificial intelligence76,77,78. Among the six models considered, both K-nearest neighbours (KNN) and stochastic gradient descent (SDG) exhibit superior performance. In contrast, random forest (RF), multinomial naive Bayes (MNB), and support vector classification (SVC) models are unable to effectively predict instances of physical sexual harassment (‘Yes’), as indicated by their precision, recall, and F1 scores being zero.

It is intended to train bidirectional LSTM characterizations from textual data by conditioning on both the left and right context at the same time. As an outcome, BERT is fine-tuned just with one supplemental output layer to produce cutting-edge models for a variety of NLP tasks20,21. The rise in increasing popularity of social media has led to a surge in trolling, hostile and insulting comments, which really is a significant problem in terms of the good and bad effects that a communication can have on a person or group of people.

Furthermore, the sheer volume of comments and the dynamic nature of online discourse may necessitate scalable and effective data collection and processing approaches. Stop words are words that relate to the most common words in a language and do not contribute much sense to a statement; thus, they can be removed without changing the sentence. Furthermore, stemming and lemmatization are the last NLP techniques used on the dataset. The two approaches are used to reduce a derived or inflected word to its root, base, or stem form. The distinction between stemming and lemmatization is that lemmatization assures that the root word (also known as a lemma) is part of the language. These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities.

The major drawback of the proposed system is that the tokenization is done based on punctuation marks and white spaces. However, due to the grammatical structure of the Urdu language, the writer may put white space between a single word such as (Khoubsorat, beautiful), which will cause the tokenizer to tokenize the single word as two words (khoub) and (sorat), which is incorrect. Section ChatGPT App “Corpus generation” describes the creation of dataset and its statistics. Section “Results analysis” analyze the experimental results and evaluation measures. VADER calculates the text sentiment and returns the probability of a given input sentence to be positive, negative, or neural. The tool can analyze data from all sorts of social media platforms, such as Twitter and Facebook.

• Integrating these insights into your social strategy helps your brand remain responsive, customer-focused‌ and aligned with market expectations.
• Buffer offers easy-to-use social media management tools that help with publishing, analyzing performance and engagement.
• Hence, it is critical to identify which meaning suits the word depending on its usage.
• Selecting the convenient representation scheme suits the application is a substantial step28.

CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programs. It is unsurprising to note a significant negative Granger causality between the Covid keyword and the consumer evaluation of the economic climate. This implies that as the Covid term becomes more prevalent and widespread in online discussions, consumers’ assessments and expectations of the Italian economic situation become increasingly pessimistic, with a bleak outlook on future employment prospects.

Therefore, we collected comments about the Hamas-Israel conflict from YouTube News channels. Next, significant NLP preprocessing operations are carried out to enhance our classification model and carry out an experiment on DL algorithms. In this paper, classification is performed using deep learning algorithms, especially RNNs such as LSTM, GRU, Bi-LSTM, and Hybrid algorithms (CNN-Bi-LSTM). During model building, different parameters were tested, and the model with the smallest loss or error rate was selected. Therefore, we conducted different experiments using different deep-learning algorithms. Furthermore, dataset balancing occurs after preprocessing but before model training and evaluation41.