Fine Food Research Articles

Text vectorization in sentiment analysis: A comparative study of TF-IDF and Word2Vec from Amazon Fine Food Reviews

Sentiment analysis is a practical tool for marketing and branding teams. Companies can collect and analyze opinions or reviews from social media platforms, blog posts, and other numerous forums. It may help them acquire positive feedback to reinforce strengths or identify negative emotions to make improvements. The research is to compare two text vectorization methods in opinion mining: Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, using Amazon Fine Food Reviews dataset. This study will use these two methods to vectorize preprocessed text data and also input the vectorized data to the emotion classification model, analyzing the performance of two methods in the emotion classification task. The consequence indicates that the former outperforms the latter in handling large datasets, particularly in distinguishing between different sentiment categories, but latter is superior in capturing the semantic relationship of words. Therefore, it is suggested that the advantages of the two methods be combined in practical applications to improve the accuracy and efficiency.

Abstractive method-based Text Summarization using Bidirectional Long Short-Term Memory and Pointer Generator Mode

With the rise of the Internet, we now have a lot of information at our disposal. We 're swamped from many sources — news, social media, to name a few, office emails. This paper addresses the problem of reading through such extensive information by summarizing it using text summarizer based on Abstractive Summarization using deep learning models, i.e. using bidirectional Long Short-Term Memory (LSTM) networks and Pointer Generator mode. The LSTM model (which is a modification of the Recurrent Neural Network) is trained and tested on the Amazon Fine Food Review dataset using the Bahadau Attention Model Decoder with the use of Conceptnet Numberbatch embeddings that are very similar and better to GloVe. Pointer Generator mode is trained and tested by the CNN / Daily Mail dataset and the model uses both Decoder and Attention inputs. But due 2 major problems in LSTM model like the inability of the network to copy facts and repetition of words the second method is, i.e., Pointer Generator mode is used. This paper in turn aims to provide an analysis on both the models to provide a better understanding of the working of the models to enable to create a strong text summarizer. The main purpose is to provide reliable summaries of datasets or uploaded files, depending on the choice of the user. Unnecessary sentences will be rejected in order to obtain the most important sentences.

A Hybrid Approach of Prediction Using Rating and Review Data

A collaborative filtering technique has proven to be the preferable approach for personalized recommendations. Traditionally, collaborative filtering recommends target items to those users who have similar tastes. The performance of collaborative filtering degrades significantly when a considerable number of users do not provide ratings on recommended products. In such a scenario, the dataset utilized in recommendation becomes highly sparse, and ratings become very few or none co-rated. To mitigate the problem, as mentioned earlier, and to improve the performance of collaborative filtering, we propose an approach that adopts users' textual reviews and ratings both in the rating prediction. The dataset used is Amazon fine Food Reviews containing rating and text review with 568454 reviews from October 1999 to October 2012. The proposed model is tested on the collected dataset. The experimental results provide the proper evidence that the proposed model outperforms other traditional algorithms of collaborative filtering techniques.

Bringing Big SynBio to the Big City

Genetic Engineering & Biotechnology NewsVol. 42, No. 8 OMICSBringing Big SynBio to the Big CityLike fine food, high fashion, and big finance, synthetic biology is on the list of things that New York City does wellJulianna LeMieux, PhDJulianna LeMieux, PhDSearch for more papers by this authorPublished Online:11 Aug 2022https://doi.org/10.1089/gen.42.08.12AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 8Aug 2022 Information© 2022 by GEN PublishingTo cite this article:Julianna LeMieux, PhD.Bringing Big SynBio to the Big City.Genetic Engineering & Biotechnology News.Aug 2022.38-40, 42.http://doi.org/10.1089/gen.42.08.12Published in Volume: 42 Issue 8: August 11, 2022PDF download

Analysis of the Complex Network of the Urban Function under the Lockdown of COVID-19: Evidence from Shenzhen in China

In this paper, the complex network of the urban functions in Shenzhen of China under the lockdown of the corona virus disease 2019 (COVID-19) is studied. The location quotient is used to obtain the dominant urban functions of the districts in Shenzhen before and under the lockdown of COVID-19. By using the conditional probability, the interdependencies between the urban functions are proposed to obtain the complex networks of urban functions and their clusters. The relationships between the urban functions, and the overall and cluster characteristics of the urban functions before and under the lockdown of COVID-19 are analyzed based on the complex networks. The mean degree and mean weighted degree of the primary categories of the urban functions are obtained to discuss the classification characteristics of the urban functions before and under the lockdown of COVID-19. Then, the differences and changes of the urban functions before and under the lockdown of COVID-19 are compared, and the corresponding policy implications under the lockdown of COVID-19 are presented. The results show that under the lockdown of COVID-19, the correlation of the urban functions is stronger than that before the lockdown; the common urban functions are more useful and essential, and finance, fine food and medical treatment are important; public service and government departments have the most positive relationship with other urban functions, and finance service has the highest spatial agglomeration distribution trend; and the cluster characteristics of urban functions are more related to people’s livelihood, and the urban functions show incomplete and cannot be operated for long term.

The Diaries of Judith Malina, 1958–1971 (Excerpt)

The Diaries of Judith Malina, 1958–1971 (Excerpt)

Elemental Analysis of Solid Food Materials Using a Reliable Laser Ablation Inductively Coupled Plasma Mass Spectrometry Method.

Quantification of trace and minor nutrient elements is crucial for maintaining human health. A reliable laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) method for food materials was developed by combining fine food powder (dm < 3 μm) pellets as the external standard with an average C content as the internal standard (IS). The finer and homogeneous aerosol produced by ablating the fine powder pellets is beneficial for transportation and analyte ionization in ICP, which helps alleviate the matrix effects and improves the analytical precision and accuracy. The average C content is 39.9 ± 1.9% for plant-derived foods (n = 22) and 46.9 ± 1.1% for animal-derived foods (n = 7). The accuracy (recovery, 80-120%) and precision (RSD, 0.5-9.8%) were validated by analyzing a series of food certified reference materials. The high-throughput method is a promising alternative for routine sample analysis in food safety laboratories.

Sentimental analysis on user’s reviews using BERT

In the current E-Commerce and M−Commerce evolved Internet world, people are expressing their sentiments on the product or service in the form of reviews and ratings. Sentiment analysis finds a contextual inference in the user sentiment which helps the E-Commerce sector and other end-users to realize the opinion of the product or service. People give their opinion in a variety of formats like reviews, blogs, news, or comments. Sentiment Analysis is used quick gaining insights using large volumes of text data which can be helpful in improving the quality of service and can make companies to earn huge profits. In sentimental analysis, modeling of sentimental relations such as word negation and word intensification are great challenge. In the proposed work, Bidirectional Encoder Representations from Transformers (BERT) is constructed for classifying user sentiments on IMDB Movie Review and Amazon Fine Food Review datasets. Word negations and intensifications are also considered for deriving the sentiment of reviews. The experimental results shows that the BERT can capture the sentimental relations effectively than basic ML, DL models and other recent literatures.

Amazon Fine Food Reviews with BERT Model

Online reviews are essential for consumers and businesses. From reading the reviews, people can learn about products intuitively. More people rely on reviews to understand products and services before their purchase. Reviews have become increasingly important, especially in the eCommerce industry. Usually, reviews consist of two parts: overall score and text description. In this research paper, we develop a model based on BERT, one of the Natural Language Processing (NLP) methods, to predict an overall review score based on the text descriptions. The dataset applied in this research is the “Amazon Fine Food Reviews” dataset on Kaggle. Before completing the model analysis, we cleansed the dataset, dropped the punctuations and special characters in comments, reselected data based on the length of reviews, and word clouds on model analysis. The model reaches an accuracy of 0.7982 and a loss value of 0.5433 in predicting the testing group. We could implant our model in food businesses to generate an overall score from text reviews collected from various sources.

The Use of Transformer Model in Opinion Summarisation

We live in an age of information, therefore collected data and documentation are practically treasure resources. All about a business and its development can be estimated with clarity via statistics. Any machine that could really analyse information to predict a projected outcome is known for being extremely vital for the business. It is critical for the system to provide accurate and useful knowledge of the products in order to conduct accurate assessment. Summarisation is a technique for obtaining a rundown from series of sentences in a study or observation that facilitates us with understanding the basic content of the knowledge expressed within. Simple and brief summaries of just a product will assist the system in performing prospective product research and development. In our paper, we use a deep learning framework that provides to extract clean, relevant, brief summaries from comprehensive customer feedback. Strategies of abstractive text summarisation is used. The method of extracting the primary keyword from a statement and using them in the summary is defined as extractive text summarisation. We utilise abstractive summarisation in this case, which evolves from sample information and provides the best feasible description. Utilising Transformer with Depth Scaling MultiHeaded Attention as well as GloVe word embedding with positional encoding, we illustrate an abstractive approach to extract summaries from the Amazon fine food reviews dataset. Transformer aids in the parallelisation of workloads in order to process data more quickly. We have used an Attention layer which boost the model's quality and enables it to become more effective. The BLUE rating is used to quantify the model's potency.

Modeling sentimental bias and temporal dynamics for adaptive deep recommendation system

Recommendation systems rely on the historic data of users' purchases and their feedbacks to profile their preferences and make future recommendations. Most of these systems usually employ Collaborative Filtering (CF) models to analyze users’ ratings and infer the latent factors which represent the user and item features in k-dimensional latent space. However, the historical rating data used for recommendations are usually sparsed and unbalanced. Various approaches have been used to resolve these issues by combining the user’s ratings and reviews to better capture the user’s sentiments and make accurate recommendations. Other challenges comprise changes in users’ preferences and items’ perceptions over time. Therefore, this paper presents a new Sentiment Scoring Model (SSM) based on Long-/Short-Term Memory and a combination function that catches the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset. Next, we proposed an Adaptive LSTM (ALSTM) method that can model the drifting of user and item features to improve the recommendation accuracy. We show the performance of our model on the three real-world rating datasets from Amazon reviews, which comprises Fine Food, Baby, and Cell-phone & Accessories categories. The result shows the superiority of our proposed model over the existing static and dynamic models. The statistical test shows that all the performance gains are significant at p < 0.05.

Effects of heat moisture treatment on the structural, physicochemical and digestibility properties of potato starch–soybean peptide complexes

SummaryIn this study, potato starch (PS) was complexed with soybean peptides (SPTs) with different molecular weights under heat moisture treatment (HMT) at different moisture contents. The results showed that PSSPT complexes formed under HMT were characterised by weaker and fuzzier polarised cross patterns, and more agglomerates. After HMT, higher gelatinisation temperatures and lower enthalpies were observed for PSSPT complexes. In particular, higher gelatinisation temperatures were observed when moisture levels were higher during HMT. Compared with physically mixed counterparts, the PSSPT complexes under HMT displayed higher pasting temperatures and lower swelling power, and peak viscosity. Higher moisture content during HMT led to a less thermodynamically stable B‐polymorphic structure converting to a more stable A‐type polymorph. Higher SDS content was found in the PSSPT complexes subjected to HMT with lower moisture content, while relatively higher RS content was associated with higher moisture content of HMT. These results could be attributed to restructuring of the amylose/amylopectin chains, the physical barrier of SPT and interactions between the negatively charged groups in PS and the side chains in SPT. This study will advance our understanding of food multicomponent interactions, as well as guidance for the application development of low‐glycaemic index foods containing SDS and RS in the field of fine food processing.

Sentiment Analysis on Amazon Fine Food Reviews by using Linear Machine Learning Models

Sentiment Analysis on Amazon Fine Food Reviews by using Linear Machine Learning Models

Raymond L. Erikson, PhD: In Memoriam (1936–2020)

![][1] > I hoed and trenched and weeded, And took the flowers to fair. > > The Shropshire Lad by A. E. Houseman In 2016, Ray started a personal reflection on his career in science titled “Four Decades of Protein Phosphorylation and Cancer Research.” Nothing more aptly fit his

A Review: Recommendation System of Indian Cuisine Recipes Using Content-Based Approach

The recommendation system has helped user’s to take quick decisions and thus gaining popularity in recent years. The Indian traditional cuisine has been extensively praised for its remarkable use of herbs and spices. The food of India is famous for its great variety of dishes. The style of cooking differs from one area to another. India is well known for its extensive diversity of fine food procurable in a wide range of eatery and hotel resorts, which recalls the unity in diversity. The proposed approach recommends Indian cuisine recipes using the content-based approach. It recommends items on the basis of the geographical areas from where the cuisines belong to and also based on the specific dish that the user likes. It also recommends items on the basis of user's geographical location.

Effective Opinion Words Extraction for Food Reviews Classification

Opinion mining (known as sentiment analysis or emotion Artificial Intelligence) holds important roles for e-commerce and benefits to numerous business and organizations. It studies the use of natural language processing, text analysis, computational linguistics, and biometrics to provide us business valuable insights into how people feel about our product brand or service. In this study, we investigate reviews from Amazon Fine Food Reviews dataset including about 500,000 reviews and propose a method to transform reviews into features including Opinion Words which then can be used for reviews classification tasks by machine learning algorithms. From the obtained results, we evaluate useful Opinion Words which can be informative to identify whether the review is positive or negative.

Elemental screening of plant-based foods by slurry nebulization ICP-MS

A reliable solid sampling ICP-MS method based on direct introduction of fine food particle slurries for the nutrition and toxic elements was developed.

Macroscopic characteristics of the gonad maturity stages of dusky parrotfish Scarus niger

The dusky parrotfish Scarus niger is an important economic fish. Like other reef fish that have good taste, white and thick meat, and don’t have a lot of tiny bones, the dusky parrotfish is a fine food fish. The dusky parrotfish has a very wide distribution in tropical and subtropical sea waters. This study aimed to validate the maturity stage descriptions based on macroscopic characters. The results of this study are expected to be a reference in the study of the reproductive biology of dusky parrotfish and other parrotfishes, such as first maturity and the reproductive cycle. The samplings were done monthly at Makassar Fisheries Port from January 2018 to May 2019. The parameters measured were total length, body weight, gonad weight, gonad length and width, and liver weight. The observed aspect was the gonad colour and condition for both male and female fishes. Five stages of gonad maturity used to identify the gonad maturity stages were quite accurate. The accuracy was indicated by the gonadosomatic index (GSI) and hepatosomatic index (HSI) values that increased progressively from Stage I to IV. This accuracy validates macroscopic characters used for identifying the maturity stage of dusky parrotfish

Automatic Text Summarizing System Using Reinforcement Learning Technique

Automatic document summarization, the process of retrieving key information from a source document is a relevant as well as challenging art. Summarization can be done based on different aspects, on which different works have been done. Extractive summarization and abstractive summarization are such two major types of summarization problems. Extractive summarization is a task in which both machine learning and deep learning techniques have been tested aiming at better results. One of the less used type of machine learning technique called reinforcement learning (RL) was previously used mostly for the task of developing video games such as Atari games and this method is later being used for different NLP tasks too. Au- tomatic summarization using reinforcement learning techniques has become one of the major tasks in the modern research area. Beginning with the traditional reinforcement learning techniques, the research has reached to a state where combination of both deep learning as well as reinforcement learning methods got used together. In this work, we propose a novel abstractive text summarizer which is built using both deep neural network and reinforcement learning technique. The generated summaries are evaluated manually and using average semantic similarity score as well. The system is tested on Amazon fine food review dataset and the results show that the generated summaries are meaningful and context dependent in most of the cases.

Julian Tudor Hart: an appreciation: Born 9 March 1927; died 1 July 2018.

Visiting Julian and Mary Hart at Glyncorrwg in South Wales in the 70s and 80s, as hundreds did, and experiencing their rich mixture of hospitality, fine food, and brilliant conversation, was to live life temporarily on a higher plane. As a visiting medical student in 1975 on my first night out, listening to men in a pub singing ‘Delilah’, ‘Myfanwy’, and Verdi’s ‘Chorus of the Hebrew Slaves’, I got an early taste of the strength in depth of a mining community. Dr ‘Art (without an H) lived in such a community with his patients: ‘From many direct and indirect contacts, in schools, shops and gossip, I have come to understand how ignorant I would be if I knew them only as a doctor seeing them when they were ill.’ (1973) Julian hated when bad things happened to his patients, especially when …