With the rise of digital technologies, marketers increasingly focus on identifying key influencers within social networks to strengthen marketing efforts and gain insights into audience behavior. These influencers play a vital role in shaping community decisions and enhancing brand visibility, trust, and sales. However, pinpointing relevant opinion leaders aligned with company goals remains challenging due to their informal status, evolving behaviors, and diverse marketing contexts. Existing research has primarily identified opinion leaders based on either user interactions or content characteristics, with limited attention given to combining both dimensions. Moreover, sentiment expressed in reactions and comments is often overlooked, despite its significance in assessing user influence. This study proposes a novel hybrid approach for detecting opinion leaders on Facebook by combining multiple metrics: the evolution of post content over time, interaction and engagement levels, opinion term extraction, and sentiment analysis of user comments. By computing a composite score that reflects both social activity and emotional tone, the proposed method offers a more comprehensive and accurate identification of influential individuals. Experimental results demonstrate its effectiveness in improving influencer detection within online communities.