Top 5 NLP Tools in Python for Text Analysis Applications

However, in real scenarios, there may not be sufficient labeled training data, and even if provided with sufficient training data, the distributions of training data and target data are almost certainly different to some extent. The Bi-GRU-CNN model showed the highest performance with 83.20 accuracy for the BRAD dataset, as reported in Table 6. In addition, the model achived nearly 2% improved accuracy compared to the Deep CNN ArCAR System21 and almost 2% enhanced F-score, as clarified in Table 7.

SAP HANA has recently introduced streamlining access administration for its alerts and metrics API feature. Through this development, users can retrieve administration information, which includes alerts for prolonged statements or metrics for tracking memory utilization. Additionally, SAP HANA has upgraded its capabilities for storing, processing, and analyzing data through built-in tools like graphs, spatial functions, documents, machine learning, and predictive analytics features. SAP HANA Sentiment Analysis lets you connect to a data source to extract opinions about products and services. You can prepare and process data for sentiment analysis with its predict room feature and drag-and-drop tool. Its interface also features a properties panel, which lets you select a target variable, and advanced panels to select languages, media types, the option to report profanities, and more.

But without resampling, the recall rate was as low as 28~30% for negative class, the precision rate for the negative class I get from oversampling is more robust at around 47~49%. Luckily cross-validation function I defined above as “lr_cv()” will fit the pipeline only with the training set split after cross-validation split, thus it is not leaking any information of validation set to the model. ChatGPT While trying to read the files into a Pandas dataframe, I found two files cannot be properly loaded as tsv file. It seems like there are some entries not properly tab-separated, so end up as a chunk of 10 or more tweets stuck together. I could have tried retrieving them with tweet ID provided, but I decided to first ignore these two files, and make up a training set with only 9 txt files.

What is social media sentiment analysis?

The study reveals that sentiment analysis of English translations of Arabic texts yields competitive results compared with native Arabic sentiment analysis. Additionally, this research demonstrates the tangible benefits that Arabic sentiment analysis systems can derive from incorporating automatically translated English sentiment lexicons. Moreover, this study encompasses manual annotation studies designed to discern the reasons behind sentiment disparities between translations and source words or texts. This investigation is of particular significance as it contributes to the development of automatic translation systems.

This section explains the results of various experiments that have been executed in this study, the usefulness of our proposed architecture for Urdu SA, and the discussion of revealed results. In the evaluation of various implemented machine learning, deep learning, and rule-based algorithms, it is observed that the mBERT algorithm perform better than all other models. According to this study45, authors used three classic machine learning algorithms, such as NB, SVM, and Decision tree followed by a supervised machine learning approach to create Word Sense Disambiguation (WSD) in Urdu text. However, by implanting an adaptive mechanism, the system’s accuracy could be increased. Another study42 used a corpus collected from the BBC Urdu news website to work on Urdu text classification.

This limitation significantly hampers the development and implementation of language-specific sentiment analysis techniques similar to those used in English. The critical components of sentiment analysis include labelled corpora and sentiment lexica. This study systematically translated these resources into languages that have limited resources. The primary objective is to enhance classification accuracy, mainly when dealing with available (labelled or raw) training instances.

Because emotions are an important feature of human nature, they have attracted a great deal of attention in psychology and other fields of study relating to human behaviour, like business, healthcare, and education (Nandwani and Verma, 2021). TextBlob is another excellent open-source library for performing NLP tasks with ease, including sentiment analysis. It also an a sentiment lexicon (in the form of an XML file) which it leverages to give both polarity and subjectivity scores.

The dataset provided in our experiment tested over a certain number of topics and features, though additional investigation would be essential to make conclusive statements. Also, we ran all the topic methods by including several feature numbers, as well as calculating the average of the recall, precision, and F-scores. As a result, the LDA method outperforms other TM methods with most features, while the RP model receives the lowest F-score in most runs in our experiments. The graphs in Figure 6 present the average results of F-scores with a different number of feature f on the 20-newsgroup dataset.

Examining factors influencing the user’s loyalty on algorithmic news recommendation service

To mitigate bias and preserve the text semantics no extensive preprocessing as stemming, normalization, and lemmatization is applied to the datasets, and the considered vocabulary includes all the characters that appeare in the dataset57,58. Also, all terms in the corpus are encoded, including stop words and Arabic words composed in English characters that are commonly removed in the preprocessing stage. The elimination of such observations may influence the understanding of the context.

Stop words and infrequent words were deleted, which increased performance for medium and small datasets but decreased performance for large corpora. According to their findings, CNN with several filters (3,4,5) outperformed the competition, whereas BiLSTM outperformed CLSTM and LSTM. The authors of47 used a single layer CNN with several filters to classify documents at the document level, and the results outperformed the baseline approaches. For document classification48, compared the performance of hybrid, machine learning, and deep learning models.

Some work has been carried out to detect mental illness by interviewing users and then analyzing the linguistic information extracted from transcribed clinical interviews33,34. The main datasets include the DAIC-WoZ depression database35 that involves transcriptions of 142 participants, the AViD-Corpus36 with 48 participants, and the schizophrenic identification corpus37 collected from 109 participants. From the angle of the US, the evolving bilateral relationship between the US and China is an issue with complex political, economic, and security dimensions (Medeiros, 2019). Due to China’s contribution to the 2007–2008 financial crisis, its financial stability became salient during this period. We chose Extract (6) to illustrate the newspaper’s portrayal of the democratic rights of the Chinese people. CDA is adopted as the theoretical base of this study because of its foci on “the relationship between language, ideology, and power, and the relationship between discourse and social change” (Fairclough, 1992, pp. 68–69).

What is Data Management?…

In this section, we give a quick overview of existing datasets and popular techniques for sentiment analysis. Social networks (SNs) such as Blogs, Forums, Facebook, YouTube, Twitter, Instagram, and others have recently emerged as the most important platforms for social communication between diverse people1,2. As technology and awareness grow, more people are using the internet for global communication, online shopping, sharing their experiences and thoughts, remote education, and correspondence on numerous aspects of life3,4,5. Users are increasingly using SNs to communicate their views, opinions, and thoughts, as well as participate in discussion groups6. The inconspicuousness of the World Wide Web (WWW) has permitted single user to engage in aggressive SNs speech data that has made text conversation7,8 or, more precisely, sentiment analysis (SA) is vital to understand the behaviors of people9,10,11,12,13,14,15.

• GloVe is computationally efficient compared to some other methods, as it relies on global statistics and employs matrix factorization techniques to learn the word vectors.
• This allows to build explicit and compact cognitive-semantic representations of user’s interest, documents, and queries, subject to simple familiarity measures generalizing usual vector-to-vector cosine distance.
• Early work on SLSA mainly focused on extracting different sentiment hints (e.g., n-gram, lexicon, pos and handcrafted rules) for SVM classifiers17,18,19,20.
• Morphological diversity of the same Arabic word within different contexts was considered in a SA task by utilizing three types of feature representation44.

In order to capture sentiment information, Rao et al. proposed a hierarchical MGL-CNN model based on CNN128. Lin et al. designed a CNN framework combined with a graph model to leverage tweet content and social interaction information129. The paper presents quantum model of subjective text perception based on binary cognitive distinctions corresponding to words of natural language.

Conduct competitive analysis

Data cleaning process is similar to my previous project, but this time I added a long list of contraction to expand most of the contracted form to its original form such as “don’t” to “do not”. And this time, instead of Regex, I used Spacy to parse the documents, and filtered numbers, URL, punctuation, etc. People can discuss their mental health conditions and seek mental help from online forums (also called online communities). There are various forms of online forums, such as chat rooms, discussion rooms (recoveryourlife, endthislife). For example, Saleem et al. designed a psychological distress detection model on 512 discussion threads downloaded from an online forum for veterans26.

However, if the algorithm simply chooses the nearest neighbour according to the n_neighbors_ver3 parameter, I doubt that it will end up with the exact same number of entries for each class. SMOTE sampling seems to have a slightly higher accuracy and F1 score compared to random oversampling. With the results so far, it seems like choosing SMOTE oversampling is preferable over original or random oversampling. I’ll first fit TfidfVectorizer, and oversample using Tf-Idf representation of texts.

MIL is a machine learning paradigm, which aims to learn features from bags’ labels of the training set instead of individual labels. However, despite our best efforts to be as objective as possible in the selection of data, some researcher subjectivity may have entered into our classification of three categories of “stability” and our method of sentence analysis. As such, future studies could develop specialized lexicons to dig out sentiment features peculiar to news discourse. Several factors influence the performance of deep learning models for instance data preparation, the size of the dataset, as well as the number of words within the sentence impact the performance of the model. When training the model using 3000 sentences of the datasets and with a limited number of words within a sentence gives an accuracy of 85.00%. As the number of words increases to greater than five words per comment within the sentence the performance improves from 85.00 to 88.66% which is a 3.6% improvement.

Get a nuanced understanding of your target audience, and effectively capitalize on feedback to improve customer engagement and brand reputation quickly and accurately. Use the data from social sentiment analytics to understand the emotional tone and preferences of your audience. Teams can craft messages that resonate more deeply, improving engagement and loyalty.

• This definition may explain the newspaper’s negative depiction of various sociopolitical issues in China such as unemployment, suppression of people’s freedom of expression, aggressive actions in East China Sea disputes, control of Hong Kong, etc.
• To avoid overfitting, the 3 epochs were chosen as the final model, where the prediction accuracy is 84.5%.
• Convolutional layers help capture more abstracted semantic features from the input text and reduce dimensionality.

This definition may explain the newspaper’s negative depiction of various sociopolitical issues in China such as unemployment, suppression of people’s freedom of expression, aggressive actions in East China Sea disputes, control of Hong Kong, etc. In the phrase following the dash in Extract (5), China is compared to other nations in terms of economic and financial stability, and the positive evaluative adjective “abundance” is used to convey this comparison. This suggests that the newspaper had at this point acknowledged China’s economic and financial strength. In Extract (4), the statement demonstrates The New York Times’ basic understanding of stability in Chinese contexts by employing the predicational strategy “means stamping out any threats to the rule of the Communist Party”.

We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods. With the aim of measuring sentiment, we conducted a preliminary analysis of sentiment in the two smaller (pre-COVID) corpora, which comprised fewer than one million words in each language (cf. Table 4).

Two datasets are used for the models implementation; the first is a hybrid combined dataset, and the second is the Book Review Arabic Dataset (BRAD). The proposed application proves that character representation can capture morphological and semantic features, and hence it can be employed for text representation in different Arabic language understanding and processing tasks. Zhang and Qian’s model improves aspect-level sentiment analysis by using hierarchical syntactic and lexical graphs to capture word co-occurrences and differentiate dependency types, outperforming existing methods on benchmarks68. In the field of ALSC, Zheng et al. have highlighted the importance of syntactic structures for understanding sentiments related to specific aspects. Their novel neural network model, RepWalk, leverages replicated random walks on syntax graphs to better capture the informative contextual words crucial for sentiment analysis.

Then we’ll end up with either more or fewer samples of majority class than minority class depending on n neighbours we set. For example, with my dataset, if I run NearMiss-3 with default n_neighbors_ver3 of 3, it will complain and the number of neutral class(which is majority class in my dataset) will be smaller than negative class(which is minority class in my dataset). So I explicitly set n_neighbors_ver3 to be 4, so that I’ll have enough majority class data at least the same number as the minority class. The top two entries are original data, and the one on the bottom is synthetic data. Instead, the Tf-Idf values are created by taking random values between the top two original data. As you can see, if the Tf-Idf values for both original data are 0, then synthetic data also has 0 for those features, such as “adore”, “cactus”, “cats”, because if two values are the same there are no random values between them.

The present study has explored the connection between sentiment and economic crises, as verbalized through the use of emotional words in two periodicals. We have confirmed that emotional polarity was moderately negative to mildly positive in both Expansión and The Economist, although the former maintained a more optimistic tone prior to the pandemic. The Bidirectional-LSTM layer receives the vector representation of the data as an input to learn features once the data has been preprocessed and the embedding component has been constructed. Bi-directional LSTM (Bi-LSTM) can extract important contextual data from both past and future time sequences.

Findings from this study show deep learning models bring improvement compared to traditional machine learning in terms of work needed for feature extraction, performance, and scalability. Manual feature engineering wasn’t used for this work; so, it eliminates extra effort that was needed for feature extraction and in addition, the models could understand the context of a given sentence. When considering the model’s performance, a small (+ 1%) but significant increase was achieved. Scalability is the main challenge for standard machine learning models while the deep learning models used in this research showed that the accuracy for the model increases as the size of the dataset for training and testing increases.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Power and status allow individuals at the top of the hierarchy to have better access to resources, such as money, food, and potential partners, as well as the ability to make decisions for themselves and others37,38. Consequently, those with high social rank experience greater control over their own outcomes and the outcomes of others, leading to increased personal agency39,40 (see also the agentic-communal model of power41). In light of this, in the current research, we sought to understand whether personal agency is reflected in the extent to which individuals use agentive language. Specifically, we aimed to explore whether various factors (social power, social rank, and participation in a depression forum) characterized by personal agency are reflected in the extent to which individuals use the passive voice. As presented in Table 7, the GRU model registers an accuracy of 97.73%, 92.67%, and 88.99% for the training, validation, and testing, which are close to the result that was obtained for BI-LSTM. Though the number of epochs considered for the GRU to get this accuracy is twice that of BI-LSTM, GRU solves the over-fitting challenge as compared to Bi-LSTM with some parameter tuning.

It then performs entity linking to connect entity mentions in the text with a predefined set of relational categories. Besides improving data labeling workflows, the platform reduces time and cost through intelligent automation. Spiky is a US startup that develops an AI-based analytics tool to improve sales calls, training, and coaching sessions.

Text sentiment analysis tools

A deep learning model based on pre-trained word embedding captures long-term semantic relationships between words, unlike rule-based and machine learning-based approaches. To answer the second question, the deep learning models were compared to the machine learning-based methods and the rule-based method of Urdu sentiment analysis. However, the current train set consists of only 70 sentences, which is relatively small. This limited size can make the model sensitive and prone to overfitting, especially considering the presence of highly frequent words like ‘rape’ and ‘fear’ in both classes.

This study investigated the effectiveness of using different machine translation and sentiment analysis models to analyze sentiments in four foreign languages. Our results indicate that machine translation and sentiment analysis models can accurately analyze sentiment in foreign languages. Specifically, Google Translate and the proposed ensemble model performed the best in terms of precision, recall, and F1 score.

Additionally, implementing boosting techniques that combine multiple machine learning models can yield a more robust and accurate outcome by considering the majority vote among these models. Furthermore, enhancing this framework can be achieved by incorporating emotion and sentiment labelling using established dictionaries. This additional layer of analysis can provide deeper insights into the context and tone of the text being analysed. Finally, expanding the size of the datasets used for training these models can significantly improve their performance and accuracy. By exposing them to larger and more diverse datasets, these models can better generalize patterns and nuances present in real-world data. Six machine learning algorithms were utilized to construct the text classification models in this study.

Additionally, some deep learning algorithms such as CNN, LSTM, Bi-LSTM, GRU and Bi-GRU with fastText embeddings were also implemented. Figure 2 explains the abstract-level framework from data collection to classification. The primary goal of pre-processing is to prepare input text for subsequent tasks using various steps such as spelling correction, Urdu text cleaning, tokenization, Urdu word segmentation, normalization of Urdu text, and stop word removal. Stop words are vital words of any dialect and have no means in the context of sentiment classifications. Due to the morphological structure of the Urdu language, the space between words does not specify a word boundary. Space-omission and Space-insertion are two main issues are linked with Urdu word segmentation.

Another experiment was conducted to evaluate the ability of the applied models to capture language features from hybrid sources, domains, and dialects. The Bi-GRU-CNN model reported the highest performance on the BRAD test set, as shown in Table 8. Results prove that the knowledge learned from the hybrid dataset can be exploited to classify samples from unseen datasets. The exhibited performace is a consequent on the fact that the unseen dataset belongs to a domain already included in the mixed dataset. In the proposed investigation, the SA task is inspected based on character representation, which reduces the vocabulary set size compared to the word vocabulary.

At the same time, there is a continued emphasis on political and government issues, with a focus on global affairs and geopolitical matters. We studied nouns, as they often represent concrete or abstract concepts, entities, or ideas, which makes them particularly useful for identifying the main topics and themes within a corpus. Nouns often provide a more stable and consistent representation of topics and tend to be more specific and less ambiguous than other parts of speech, such as adjectives or verbs.

The recurrence connection in RNNs supports the model to memorize dependency information included in the sequence as context information in natural language tasks14. And hence, RNNs can account for words order within the sentence enabling preserving the context15. Unlike feedforward neural networks that employ the learned weights for output prediction, RNN uses the learned weights semantic analysis of text and a state vector for output generation16. Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bi-directional Long-Short Term Memory (Bi-LSTM), and Bi-directional Gated Recurrent Unit (Bi-GRU) are variants of the simple RNN. Machine learning models, on average, contain less trainable parameters than deep neural networks, which explains why they train so quickly.

Social sentiment analysis provides insights into what resonates with your audience, allowing you to craft messages that are more likely to engage and convert. • LDA, introduced by Blei et al. (2003), is a probabilistic model that is considered to be the most popular ChatGPT App TM algorithm in real-life applications to extract topics from document collections since it provides accurate results and can be trained online. Corpus is organized as a random mixture of latent topics in the LDA model, and the topic refers to a word distribution.

“Software framework for topic modelling with large corpora,” in Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks (Valletta), 46–50. Recommended search of documents from conversation with relevant keywords using text similarity. • The F-score (F) measures the effectiveness of the retrieval and is calculated by combining the two standard measures in text mining, namely, recall and precision. • For other open-source toolkits besides those mentioned above, David Blei’s Lab provides many TM open-source software that is available in GitHub such as online inference for HDP in the Python language and TopicNets (Gretarsson et al., 2012).

The reasons for the changes will be explained in detail in the following sub-sections. Since the news articles considered in this work are written in Italian, we used a BERT tokenizer to pre-process the news articles and a BERT model to encode them; both pre-trained on a corpus including only Italian documents. When the organization determines how to detect positive and negative sentiment in customer expressions, it can improve its interactions with the customer. By exploring historical data on customer interaction and experience, the company can predict future customer actions and behaviors, and work toward making those actions and behaviors positive.