10 Best Python Libraries for Sentiment Analysis 2024

Compared to measurements using purely syntactic components, such measurements focusing on semantic roles can better indicate substantial changes in information quantity. These indices are intended to detect information gaps resulting from syntactic subsumption, which often takes the form of either an increase in number of semantic roles or an increase in the length of a single semantic role. It indicates that the introduction of jieba lexicon can cut Chinese danmaku text into more reasonable words, reduce noise and ambiguity, and improve the quality of word embedding.

As an emerging information carrier, danmaku contains rich and real semantic information, which is an important corpus for sentiment analysis4, and the sentiment analysis of danmakus has important academic and commercial value. The ablation study results reveal several important insights about the contributions of various components to the performance of our model. Firstly, it is evident that the complete model configuration comprising refinement processes, syntactic features, and the integration of the MLEGCN and attention modules-consistently yields the highest F1 scores across both the Res14 and Lap14 datasets. This underscores the synergy between the components, suggesting that each plays a crucial role in the model’s ability to effectively process and analyze linguistic data. Particularly, the removal of the refinement process results in a uniform decrease in performance across all model variations and datasets, albeit relatively slight.

Both types of sexual harassment are often justified or normalized by the harassers as a way of expressing their masculinity and asserting their dominance. With sentiment analysis, there’s no second-guessing what people think about your brand. Implementing regular sentiment analysis into your strategy improves your understanding of customer perceptions and enables you to make informed, data-driven decisions that drive business success​.

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

This platform also provides real-time decision-making, which allows businesses to back up their decision processes and strategies with robust data and incorporate them into specific actions within the SAP ecosystem. Talkwalker has a simple and clean dashboard that helps users monitor social media conversations about a new product, marketing campaign, brand reputation, and more. It offers a quick brand overview that includes KPIs for engagement, volume, sentiment, demographics, and geography. Users can also access graphs for real-time trends and compare multiple brands to easily benchmark against competitors.

Since the correlation between the front and back of a sequence cannot be described, traditional machine learning is ineffective in handling sequence learning. Sequence learning models such as recurrent neural networks (RNNs) which link nodes between hidden layers, enable deep learning algorithms to learn sequence features dynamically. RNNs, a type of deep learning technique, have demonstrated efficacy in precisely capturing these subtleties. Taking this into account, we suggested using deep learning algorithms to find YouTube comments about the Palestine-Israel War, since the findings will help Palestine and Israel find a peaceful solution to their conflict. Section “Proposed model architecture” presents the proposed method and algorithm usage. Section “Conclusion and recommendation” concludes the paper and outlines future work.

Proposed methodology

Additionally, in 1917, Britain supported the Zionist movement, leading to tensions with Arabs after WWI. The Arab uprising in 1936 ended British support, resulting in Arab independence5. Several ChatGPT App companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them.

The model struggle to distinguish sarcasm, figurative speech and sentiment sentences that contain both words that give positive and negative sentiment. Ghorbani et al.10 introduced an integrated architecture of CNN and Bidirectional Long Short-Term Memory (LSTM) to assess word polarity. Despite initial setbacks, performance improved to 89.02% when Bidirectional LSTM replaced Bidirectional GRU. Mohammed and Kora11 tackled sentiment analysis ChatGPT for Arabic, a complex and resource-scarce language, creating a dataset of 40,000 annotated tweets. They employed various deep learning models, including CNN and Long Short-Term Memory (LSTM), achieving accuracy rates ranging from 72.14 to 88.71% after data augmentation. Hassan and Mahmood9 employed deep learning for sentiment analysis on short texts using datasets like Stanford Large Movie Review (IMDB) and Stanford Sentiment Treebank.

GML fulfills gradual learning by iterative factor inference over a factor graph consisting of the labeled and unlabeled instances and their common features. At each iteration, it typically labels the unlabeled instance with the highest degree of evidential certainty. Sentiment analysis is a highly powerful tool that is increasingly being deployed by all types of businesses, and there are several Python libraries that can help carry out this process. To have a better understanding of the nuances in semantic subsumption, this study inspected the distribution of Wu-Palmer Similarity and Lin Similarity of the two text types.

In the current study, such eclectic features are also found at the syntactic-semantic level, indicating that the negotiation in the complex translation process also has an impact on the semantic characteristic of the translated texts. This supports Krüger’s (2014) view that S-universal and T-universal are caused by different factors. One plausible explanation for these findings might be the Hypothesis of Gravitational Pull posited by Halverson (2003, 2017), which assumes that translated language is affected by three types of forces. One force is the “magnetism effect” of the target language that comes from prototypical or highly salient linguistic forms.

The three layers Bi-LSTM model trained with the trigrams of inverse gravity moment weighted embedding realized the best performance. A hybrid parallel model that utlized three seprate channels was proposed in51. Character CNN, word CNN, and sentence Bi-LSTM-CNN channels were trained parallel. A positioning binary embedding scheme (PBES) was proposed to formulate contextualized embeddings that efficiently represent character, word, and sentence features.

Thus you can see it has identified two noun phrases (NP) and one verb phrase (VP) in the news article. There is no universal stopword list, but we use a standard English language stopwords list from nltk. Do note that the lemmatization process is considerably slower than stemming, because an additional step is involved where the root form or lemma is formed by removing the affix from the word if and only if the lemma is present in the dictionary. To understand stemming, you need to gain some perspective on what word stems represent.

As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use. The training objective is to maximize the likelihood of the actual context words given the target word. This involves adjusting the weights of the embedding layer to minimize the difference between the predicted probabilities and the actual distribution of context words. It can be adjusted based on the specific requirements of the task, allowing users to capture both local and global context relationships. Given a sequence of words in a sentence, the CBOW model takes a fixed number of context words (words surrounding the target word) as input.

Results analysis

The general area of sentiment analysis has experienced exponential growth, driven primarily by the expansion of digital communication platforms and massive amounts of daily text data. You can foun additiona information about ai customer service and artificial intelligence and NLP. However, the effectiveness of sentiment analysis has primarily been demonstrated in English owing to the availability of extensive labelled datasets and the development of sophisticated language models6. This leaves a significant gap in analysing sentiments in non-English languages, where labelled data are often insufficient or absent7,8. For the present study, we adopted a corpus-based methodology, which involved compiling a representative sample of the material under examination, plus the use of a series of electronic tools to extract quantitative and qualitative data.

The result is more precise estimation of subjective relevance judgments leading to better composition of search result pages40,41,42,43. Quantum theory allows to describe semantic function of language quantitatively. In short, semantic fields of words are represented by superposition potentiality states, actualizing into concrete meanings during interaction with particular contexts.

The results of this study have implications for cross-lingual communication and understanding. If Hypothesis H is supported, it would signify the viability of sentiment analysis in foreign languages, thus facilitating improved comprehension of sentiments expressed in different languages. The findings of this research can be valuable into various domains, such as multilingual marketing campaigns, cross-cultural analysis, and international customer service, where understanding sentiment in foreign languages is of utmost importance. Currently, NLP-based solutions struggle when dealing with situations outside of their boundaries. Therefore, AI models need to be retrained for each specific situation that it is unable to solve, which is highly time-consuming. Reinforcement learning enables NLP models to learn behavior that maximizes the possibility of a positive outcome through feedback from the environment.

At this level of modeling, numerous intricacies of human cognition are hidden, but continue to affect observable behavior (cf.76). Further sections illustrate this modeling approach on the process of subjective text perception. According to psycho-physiological parallelism54, modern cognitive science builds on fusion of physical and information descriptions outlined above, constituting complementary sides of the same phenomena55,56,57,58,59,60,61,62,63.

Comparing results by periodical during the pandemic, the English sample shows a considerable increase in negative items in relation to the pre-COVID samples. In the Spanish case, the most notable decrease observed in the second period is that of positive words. Whereas in the pre-COVID period, 64% of the words were positive, during the COVID period there was a relative balance (76 positive vs. 82 negative words, 48% vs. 51%). It seems that the Spanish Newspaper Expansión does not want to create alarm among its readership, and this leads to the use of positive and negative lexis in roughly equal proportions. The English periodical is negative in both periods, as we have noted, but significant variations are seen between the pre-COVID and COVID periods, with a notable increase in negative (from 151 to 306) and positive (from 42 to 102) items in the second. It should be borne in mind that the emotional activity in both periodicals is ‘very intense’ in both periods.

Previous studies highlight how patriarchal norms and traditional gender roles contribute to gender harassment in this region. In particular, the cultural emphasis on modesty and honour perpetuates gender harassment by placing blame on women for their attire or behaviour. The concept of “honour” has become a tool for controlling women’s actions and justifying harassment (Asl, 2022, 2020; Asl and Hanafiah, 2023; Chew and Asl, 2023; Yan and Asl, 2023).

At FIRE 2021, the results were given to Dravidian Code-Mix, where the top models finished in the fourth, fifth, and tenth positions for the Tamil, Kannada, and Malayalam challenges. Word embedding models such as FastText, word2vec, and GloVe were integrated with several weighting functions for sarcasm recognition53. The deep learning structures RNN, GRU, LSTM, Bi-LSTM, and CNN were used to classify text as sarcastic or not.

• There are about 60,000 sentences in which the labels of positive, neutral, and negative are used to train the model.
• The model starts with a Glove word embedding as the embedding layer and is followed by the LSTM and GRU layers.
• Therefore, stemming and lemmatization were not applied in this study’s data cleaning and pre-processing phase, which utilized a Transformer-based pre-trained model for sentiment analysis.
• CNN-Bi-LSTM uses the capability of both models to classify the dataset, which is CNN that is well recognized for feature selection, while Bi-LSTM enables the model to include the context by providing past and future sequences.

When compared to bigram and trigram word features, all machine learning classifiers perform better using unigram word features which is consistent with50.The outcomes of several machine learning methods using character gram features are represented in Table 7. Using the Char-3-gram feature, the findings demonstrated that NB and SVM outperformed all other machine learning classifiers with an accuracy of 68.29% and 67.50% respectively. On the other hand, LR had the poorest performance, with an accuracy of 58.40% when employing the char-5-gram feature. They are respectively based on sentence-level semantic role labelling tasks and textual entailment tasks.

This research contributes to developing a state-of-the-art Arabic sentiment analysis system, creating a new dialectal Arabic sentiment lexicon, and establishing the first Arabic-English parallel corpus. Significantly, this corpus is independently annotated for sentiment by both Arabic and English speakers, thereby adding a valuable resource to the field of sentiment analysis. The simple default classifier I’ll use to compare performances of different datasets will be the logistic regression. From my previous sentiment analysis project, I learned that Tf-Idf with Logistic Regression is a pretty powerful combination. Before I apply any other more complex models such as ANN, CNN, RNN etc, the performances with logistic regression will hopefully give me a good idea of which data sampling methods I should choose. If you want to know more about Tf-Idf, and how it extracts features from text, you can check my old post, “Another Twitter Sentiment Analysis with Python-Part5”.

Our sample size is limited, which means that our analysis only serves as an indication of the potential of textual data to predict consumer confidence information. It is important to note that our findings should not be considered a final answer to the problem. The Consumer Confidence series have a monthly frequency, whereas our predictor variables are weekly data series. In order to use the leading information coming from ERKs, we transformed the monthly time series into weekly data points using a temporal disaggregation approach56. The primary objective of temporal disaggregation is to obtain high-frequency estimates under the restriction of the low-frequency data, which exhibit long-term movements of the series. Given that the Consumer Confidence surveys are conducted within the initial 15 days of each month, we conducted a temporal disaggregation to ensure that the initial values of the weekly series were in line with the monthly series.

LSTM65 is a recurrent neural network design that displays state-of-the-art sequential data findings. The LSTM model acquires the current word’s input for each time step, and the prior or last word’s output creates an output, which is utilized to feed to the next state. The prior state’s hidden layer (and, in some cases, all hidden layers) is then used for classification.We use Bi-LSTM model to classify each comment according to its class.

Aside from the TM method comparison, the graphs show that a higher F-score was obtained with the LDA model. In addition, over the Facebook conversation data, the LDA method defines the best and clearest meaning compared to other examined TM methods. To ensure that the data were ready to be trained by the deep learning models, several NLP techniques were applied. Preprocessing not only reduces the extracted feature space but also improves the classification accuracy40.

Section “Results” showcases the primary findings, subsequently analyzed in Section “Discussion and conclusions”. Sentiment analysis software may also detect emotional descriptors, such as generous, irritating, attractive, annoyed, charming, creative, innovative, confusing, lovely, rewarding, broken, thorough, wonderful, atrocious, clumsy and dangerous. These are semantic analysis of text just a few examples in a list of words and terms that can run into the thousands. If you do not have access to a GPU, you are better off with iterating through the dataset using predict_proba. We will iterate through 10k samples for predict_proba make a single prediction at a time while scoring all 10k without iteration using the batch_predict_proa method.

Step 2. Choose your terms for sentiment analysis

In my previous project, I split the data into three; training, validation, test, and all the parameter tuning was done with reserved validation set and finally applied the model to the test set. Considering that I had more than 1 million data for training, this kind of validation set approach was acceptable. But this time, the data I have is much smaller (around 40,000 tweets), and by leaving out validation set from the data we might leave out interesting information about data.

Ultimately, the success of your AI strategy will greatly depend on your NLP solution. Read eWeek’s guide to the best large language models to gain a deeper understanding of how LLMs can serve your business. We also tested the association between sentiment captured from tweets and stock market returns and volatility. The type of values we were getting from the VADER analysis of our tweets are shown in Table 1. The datasets generated during and/or analysed during the current study are available from the corresponding author upon reasonable request.

Additionally, Idiomatic has added a sentiment score tool that calculates the score per ticket and shows the average score per issue, desk channel, and customer segment. We chose Azure AI Language because it stands out when it comes to multilingual text analysis. It supports extensive language coverage and is constantly expanding its global reach. Additionally, its pre-built models are specifically designed for multilingual tasks, providing highly accurate analysis.

This lower number of words was necessary due to the limitations of the Lingmotif 2 softwareFootnote 6 (Moreno-Ortiz, 2021). Its basic function is to determine the semantic orientation of a text, that is, the extent to which it can be said to be positive or negative, by detecting the positivity or negativity contained in the different linguistic expressions in the text(s) analysed. It differs from some other opinion-mining tools because the system supports the processing of longer texts, not just mini-texts such as tweets. Emotion and sentiment are essential elements in people’s lives and are expressed linguistically through various forms of communication, not least in written texts of all kinds (news, reports, letters, blogs, forums, tweets, micro-bloggings, etc.).

As a result of its policy of reform and opening up over the past four decades, the rise of China has garnered a great deal of attention from US media as well as scholars in the social sciences, particularly those in the fields of media and discourse studies. According to a bibliometric study of news discourse analysis from 1988 to 2020, “China” was one of the top 30 keywords from 1988 through 2000 and from 2008 through 2020. In addition, from 2001 through 2007, the keyword “Hong Kong” ranked 24th, indicating that China-related issues have been salient to news discourse analysts since 1988 (Wang et al. 2022). This article investigates the antecedents of consumer confidence by analyzing the importance of economic-related keywords as reported on online news.