This study extends the ANEW norms, which originally included 1,034 words, to nearly 14,000 English lemmas, providing a richer source of information for researchers. The new norms cover a wide range of emotional dimensions, including valence (pleasantness), arousal (intensity), and dominance (control exerted by the stimulus). The data were collected through Amazon Mechanical Turk, with participants rating words on a 9-point scale. The study found that valence and dominance ratings were relatively consistent across participants, while arousal ratings were more variable. Correlations between the dimensions revealed a U-shaped relationship between valence and arousal, and between arousal and dominance. The study also explored gender differences in emotional ratings, finding that males gave higher ratings in all dimensions than females, particularly for arousal. Additionally, the study examined the impact of demographic factors such as age, gender, and education on emotional ratings, finding significant effects. The findings have implications for various fields, including emotion research, word recognition, and text-based sentiment analysis.This study extends the ANEW norms, which originally included 1,034 words, to nearly 14,000 English lemmas, providing a richer source of information for researchers. The new norms cover a wide range of emotional dimensions, including valence (pleasantness), arousal (intensity), and dominance (control exerted by the stimulus). The data were collected through Amazon Mechanical Turk, with participants rating words on a 9-point scale. The study found that valence and dominance ratings were relatively consistent across participants, while arousal ratings were more variable. Correlations between the dimensions revealed a U-shaped relationship between valence and arousal, and between arousal and dominance. The study also explored gender differences in emotional ratings, finding that males gave higher ratings in all dimensions than females, particularly for arousal. Additionally, the study examined the impact of demographic factors such as age, gender, and education on emotional ratings, finding significant effects. The findings have implications for various fields, including emotion research, word recognition, and text-based sentiment analysis.