# AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context
## 4 Results
### Analysis of Article Volume, Origin, and Access Model
#### 4.1.8 Sentiment Polarity Analysis  
The sentiment analysis of the top 100 most cited articles was conducted using the _TextBlob_ library (Figure 5), focusing on the polarity of each article's abstract to assess its emotional tone. Polarity scores provide a quantitative measure of the sentiment conveyed in the abstracts. The analysis revealed a spectrum of sentiment scores, with an overall trend toward neutrality. The numerical values in Figure 5 represent the sentiment polarity scores assigned to the abstracts of the top 100 most cited articles, calculated using the TextBlob library. These scores range from \(-1\) (very negative) to \(1\) (very positive) and are displayed along the x-axis, indicating the emotional tone conveyed in the articles. The y-axis represents the number of articles corresponding to each specific sentiment score, providing a visual distribution of emotional tones within the analyzed publications. The total of the y-axis values sums to 100, reflecting the complete set of articles included in the sentiment analysis. TextBlob performs sentiment analysis using a lexicon-based approach, assigning polarity scores to predefined words and phrases. This method identifies emotional tone by detecting terms with affective connotations. Given that scientific writing should maintain objectivity, the sentiment score analysis presented here, visualized through a bar graph, enables the detection of outliers with significant deviations. Identifying emotional biases in  
Figure 5: Sentiment polarity analysis of abstracts from the top 100 most cited scientific publications. highly cited works allows for the assessment of whether their tone aligns with the expected neutrality, ensuring compliance with impartiality standards in scientific research. The most positively polarized article, with a sentiment score of 0.317949 and 226 citations, discusses the use of a support vector machine for modeling and simulation in manufacturing, highlighting advancements in this field. On the opposite end, the most negatively polarized article, with a sentiment score of \(-0.180769\) and 177 citations, addresses the challenges of shallow landslide susceptibility mapping, emphasizing the damage caused to buildings and infrastructure by such natural disasters. Regarding Spanish publications, the most positively polarized article from Spain, titled \"An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario\" [82], with a sentiment score of 0.140504 and 209 citations, focuses on the development of a platform for monitoring sustainable environments. This emphasis on environmental monitoring aligns with Spain's ongoing efforts to improve water management and ensure sustainability in agriculture. Additionally, it reflects the country's growing interest in sustainable agricultural practices, particularly in optimizing irrigation systems and reducing environmental impacts through technological innovations. In contrast, the most negatively polarized Spanish article, titled \"More than one million barriers fragment Europe's rivers\" [83], with a sentiment score of \(-0.021652\) and 367 citations, addresses the fragmentation of Europe's rivers by over one million barriers, highlighting the negative impacts on biodiversity. This concern is particularly relevant to Spain, where river fragmentation affects water availability for irrigation--an issue critical to the country's agricultural sector. The coherence between these findings and Spain's dual focus on sustainable agriculture and water management underscores the significance of addressing water-related challenges in both environmental and agricultural contexts. Moreover, the use of sentiment analysis techniques in bibliographic analysis proves valuable in uncovering underlying trends and attitudes within the research community.