Yet Another Twitter Sentiment Analysis Part 1 tackling class imbalance by Ricky Kim

Moreover, the LSTM neurons are split into two directions, one for forward states and the other for backward states, to form bidirectional LSTM networks32. Therefore, Bidirectional LSTM networks use input from past and future time frames to minimize delays but require additional steps for backpropagation over time due to the noninteracting nature of the two directional neurons33. IBM’s Watson provides a conversation service that uses semantic analysis (natural language understanding) and deep learning to derive meaning from unstructured data.

The selection of a model for practical applications should consider specific needs, such as the importance of precision over recall or vice versa. The first of these datasets, referred to herein as Dataset 1 (D1), was introduced in a study by Wu et al. under the 2020a citation. The second dataset, known as Dataset 2 (D2), is the product of annotations by Xu et al. in 2020. It represents an enhanced and corrected version of an earlier dataset put forth by Peng et al. in 2020, aiming to rectify previous inaccuracies79,90,91.

As shown in Table 14, Logistic regression (LR) gained higher accuracy in compared to other algorithms. MonkeyLearn is a machine learning platform that offers a wide range of text analysis tools for businesses and individuals. With MonkeyLearn, users can build, train, and deploy custom text analysis models to extract insights from their data. The platform provides pre-trained models for everyday text analysis tasks such as sentiment analysis, entity recognition, and keyword extraction, as well as the ability to create custom models tailored to specific needs. Sentiments are then aggregated to determine the overall sentiment of a brand, product, or campaign.

In Period 4, the percentages of positive and negative sentiment are 58.2 and 38.4%, respectively. However, as stated previously, the sentiment value of the extracted sentences remains negative, despite the fact that the number of positive sentiment sentences exceeds the number of negative sentiment sentences. Similar to Period 3, the positive sentences relate to China’s economy and financial management, as illustrated in Extract (7). In contrast, the newspaper’s negative sentiment focuses on a number of sociopolitical issues, such as cybersecurity, COVID-19, and social unrest in Hong Kong, among others, as evidenced in Extract (8). The second step in our analysis was to examine variations in journalistic perspective across the four time periods. To calculate text polarity at the sentence level and determine the sentiment values of each period’s news stories, we used the R Package “sentiment” software (Rinker, 2018).

In the CNN experimentation, we began by inputting the preprocessed data into the CNN layer to facilitate feature extraction. The CNN layer employed 128 filters with 5 kernels and utilized the ReLU activation function. Following this feature extraction step, the data was forwarded to the GlobalMaxPooling1D layer, which downed sample the representation by selecting the maximum value across time, converting the output from 2 to 1D. To maintain output values between 0 and 1 for the binary classification task of negative and positive sentiment, a sigmoid activation function was applied. During training, the researcher measured accuracy, recall, and precision as performance metrics and conducted training over 10 epochs to optimize the model.

MonkeyLearn is a simple, straightforward text analysis tool that lets you organize, label and visualize data like customer feedback, surveys and more. View the average customer sentiment around your brand and track sentiment trends over time. Filter individual messages and posts by sentiment to respond quickly and effectively. These tools can pull information from multiple sources and employ techniques like linear regression to detect fraud and authenticate data. They also run on proprietary AI technology, which makes them powerful, flexible and scalable for all kinds of businesses. We’re talking about analyzing thousands of conversations, brand mentions and reviews spread across multiple websites and platforms—some of them happening in real-time.

A Python library named contractions is used to expand the shortened words in sentences. Expanding contractions are done to aid the recognition of grammatical categories in POS tagging. To understand how social media listening can transform your strategy, check out Sprout’s social media listening map. It will show you how to use social listening for org-wide benefits, staying ahead of the competition and making meaningful audience connections. Here’s an example of positive sentiment from one of Girlfriend Collective’s product pages.

Corpus characteristics

Wright et al. (2017) also employed a corpus linguistic method to analyse patterns in children’s descriptions of street harassment experienced. To do this, they collected children’s reports of street harassment from web-based applications and extracted comments from these reports, which were stored in plain text (.txt) files. They focused on analysing behaviour and actions by identifying all verbs in the corpus using AntConc, a corpus analysis toolkit for text analysis. These 137 different verbs were manually categorized based on types of harassment such as verbal interaction, non-verbal interaction, physicality, etc.

• Danmakus are user-generated comments that overlay on videos, enabling real-time interactions between viewers and video content.
• Additionally, we implement a refining strategy that utilizes the outcomes of aspect and opinion extractions to enhance the representation of word pairs.
• Our experiments have demonstrated that the performance of supervised GML is robust w.r.t the value of k provided that it is set within a reasonable range (between 1 and 9).
• Qualtrics is an experience management platform that offers Text iQ—a sentiment analysis tool that leverages advanced NLP technology to analyze unstructured data from various sources, including social media, surveys and customer support interactions.

They mitigate processing errors and work continuously, unlike human virtual assistants. Additionally, NLP-powered virtual assistants find applications in providing information to factory workers, assisting academic research, and more. What follows are six ChatGPT prompts to improve text for search engine optimization and social media. TextBlob is a Python (2 and 3) library that is used to process textual data, with a primary focus on making common text-processing functions accessible via easy-to-use interfaces. Objects within TextBlob can be used as Python strings that can deliver NLP functionality to help build text analysis applications. Text analysis web applications can be easily deployed online using a website builder, allowing products to be made available to the public with no additional coding.

Model design

Given the comparison between CT and ES, this could result from “the source language shining-through hypothesis”, which is defined as the source language’s interference with the translation process (Teich, 2003). It can cause the translation to retain some of the lexical and grammatical features of the source language (Dai & Xiao, 2010; Xiao, 2015). As discussed in previous sections, syntactic-semantic structures in ES have significant complexity characterized by nominalization and syntactic nestification. Although most syntactic-semantic structures are simplified through denominalization and divide translation in the translation process, a small portion of the sentences in CT retain the features of syntactic subsumption of ES.

The compound score is derived by summing the sentiment scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between –1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if we want a single uni-dimensional measure of sentiment for a given sentence. At about the same time (Loughran and McDonald, 2011) applied sentiment analysis to the so-called 10-K filings. They found that almost three-quarters of negative word counts in 10-K filings based on the Harvard dictionary are typically not negative in a financial context. To do so, they developed an alternative dictionary that better reflects sentiment in a financial text. The main goal of sentiment analysis is to determine the sentiment or feeling conveyed in text data and categorize it as positive, negative, or neutral.

The most significant work50 has recently been performed on SA of Urdu text using various machine learning and deep learning techniques. Initially, Urdu user reviews of six various domains were collected from various social media platforms to build a state of art corpus. Finally, a set of machine learning algorithms such as RF, NB, SVM, AdaBoost, MLP, LR, and deep learning algorithms such LSTM and CNN-1D were applied to validate the generated Urdu corpus.

I specifically defined k_neighbors as 1 for this toy data, since there are only two entries of negative class, if SMOTE chooses one to copy, then only one other negative entry left as a neighbour. My toy data has 5 entries in total, and the target sentiments are three positives and two negatives. In order to be balanced, semantic analysis of text this toy data needs one more entry of negative class. In order to train my sentiment classifier, I need a dataset which meets conditions below. I finished an 11-part series blog posts on Twitter sentiment analysis not long ago. I wanted to extend further and run sentiment analysis on real retrieved tweets.

This article does not contain any studies with human participants performed by any of the authors; therefore, Informed consent was not required. This article does not contain any studies with human participants performed by any of the authors; therefore, ethical approval was not required. The dataset can be available upon request to any of the authors or the corresponding author Pantea Keikhosrokiani. Histogram and density plot of the numeric value of the compound sentiment by sexual offence types.

A central feature of Comprehend is its integration with other AWS services, allowing businesses to integrate text analysis into their existing workflows. Comprehend’s advanced models can handle vast amounts of unstructured data, making it ideal for large-scale business applications. You can foun additiona information about ai customer service and artificial intelligence and NLP. It also supports custom entity recognition, enabling users to train it to detect specific terms relevant to their industry or business.

Which sentiment analysis software is best for any particular organization depends on how the company will use it. Another business might be interested in combining this sentiment data to guide future product development, and would choose a different sentiment analysis tool. The importance of customer sentiment extends to what positive or negative sentiment the customer expresses, not just directly to the organization, but to other customers as well.

Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F-score, and topic coherence. As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results. The paper sheds light on some common topic modeling methods in a short-text context and provides direction for researchers who seek to apply these methods. Sentence-level sentiment analysis (SLSA) aims to identify the overall sentiment polarity conveyed in a given sentence. The state-of-the-art performance of SLSA has been achieved by deep learning models.

EmoLex and Plutchik’s emotions

In contrast, in sentiment analysis, sentiment-related words are identified by specific software, which automatically calculates their value in a broad context. For this reason, sentiment analysis is exclusively quantitative and is therefore an ideal method for efficiently processing a large volume of text. CDA investigates the interwoven nature of inequality, authority, and ideology at various linguistic levels of discourse (Fowler, 1991; van Dijk, 2009). To address the criticism of “cherry picking” by other scholars in the social sciences, Hardt-Mautner (1995) introduced the techniques of CL to CDA in her study of news reporting on the European Union in the British press. Later, Baker et al. (2008) further advocated a “useful methodological synergy” of CL and CDA. Since then, corpus-based or corpus-assisted CDA has been employed extensively in news discourse analysis over the past two decades.

In \(S_1\), the BERT model fails to detect the positive polarity of the combination of “not” and “long”. This section contains the experimental description of applied machine learning, rule-based, deep learning algorithms and our proposed two-layer stacked Bi-LSTM model. These algorithms have been trained and tested on our proposed UCSA-21 corpus and UCSA50 datasets which are publically available. In this study49, authors recently suggested a model for Urdu SA by examining deep learning methods along with various word embeddings.

That is to say, translation universals at the syntactic-semantic level, such as explicitation and simplification, can be further distinguished depending on whether the syntactic-semantic feature presents the same or opposite results for S-universal and T-universal. This further suggests that even the translation universal under the same sub-hypothesis, like explicitation as S-universal, can be attributed to different causes. Overall, the Hypothesis of Gravitational Pull ChatGPT App provides a framework for explaining the eclectic characteristics of syntactic-semantic features in the translated texts. This results in a distinct syntactic-semantic characteristic of translations that may deviate from both source and target languages, hence an eclecticism. For core arguments, the results show that the syntactic-semantic structures of CT are more complex than those of CO, with ANPV and ANPS of all the core arguments being significantly higher.

Different machine learning and deep learning models are used to perform sentimental analysis and offensive language identification. Preprocessing steps include removing stop words, changing text to lowercase, and removing emojis. These embeddings are used to represent words and works better for pretrained deep learning models. Embeddings encode the meaning of the word such that words that are close in the vector space are expected to have similar meanings. By training the models, it produces accurate classifications and while validating the dataset it prevents the model from overfitting and is performed by dividing the dataset into train, test and validation. The set of instances used to learn to match the parameters is known as training.

T1 is the last representation of the first token or word of every sentence in Fig. The classification layer has a dimension of K x H, where K is the number of classes (Positive, negative and neutral) and H is the size of the hidden state. ChatGPT The most exciting aspect of GRU is that it can be properly trained to keep information for an extended period of time without losing track of timestamps. One takes information in a forward direction, whereas the other takes it backwards.

Also, many issues exist in TM approaches with short textual data within OSN platforms, like slang, data sparsity, spelling and grammatical errors, unstructured data, insufficient word co-occurrence information, and non-meaningful and noisy words. For example, Gao et al. (2019) discussed the problem of word sense disambiguation by using local and global semantic correlations, achieved by a word embedding model. Yan et al. (2013) developed a short-text TM method called biterm topic model (BTM) that uses word correlations or embedding to advance TM. The fundamental steps involved in text mining are shown in Figure 1, which we will explain later on our data preprocessing step. People nowadays tend to rely heavily on the internet in their daily social and commercial activities. Indeed, the internet has increased demand for the development of commercial applications and services to provide better shopping experiences and commercial activities for customers around the world.

Considering the difference between Chinese and English semantic role tagsets, the current study chose some important and relatively frequent semantic roles as research focuses. The tagsets for both Chinese and English semantic role labelling of core arguments and semantic adjuncts are quite similar. Core arguments are labeled as ArgN or AN with N being numbers representing different types of relationships. For example, A0 represents the agent/causer/experiencer of the verb and A1 represents the patient and recipient of the verb. Semantic adjuncts are roles that are not directly related to the verb, typically determiners or roles that provide supplementary information about verbs and core arguments. Common semantic adjuncts include adverbials (ADV), manners (MNR), and discourse markers (DIS).

The obtained results reveal that our proposed model fine-tuned based on mBERT with SoftMax supersedes all other deep learning models with accuracy, precision, recall, and F1 score of 77.61%, 76.15%, 78.25%, and 77.18% respectively. It is Observed that Bi-LSTM and Bi-GRU can be effective for Urdu sentiment analysis compared to other traditional machine learning, rule-based, and deep learning algorithms merely because Bi-LSTM and Bi-GRU can capture information from backward and forward ways. Bi-LSTM produces slightly better results because it understands context better than LSTM and CNN-1D.

Since the number of even single-word concepts in cognition of adult human is very large, each concept is passive most of the time, but may be activated by internal or external stimuli acquired e.g. from verbal or visual channels. This paper considers a particular class of such stimuli which are texts in natural language. Lingmotif 2 (Moreno-Ortiz, 2021) uses a scale from 0 to 100 to categorize texts, from ‘extremely negative’ to ‘extremely positive’, based on the semantic orientation of the sentiment detected in the text (Text Sentiment Score, or TSS). The TSS calculates the polarity of each sentence, taking into account both the number and the position of sentiment-related items.